Indian_Startup_EDA_C1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afcc6ef7",
   "metadata": {},
   "source": [
    "# Indian Startup Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc09b711",
   "metadata": {},
   "source": [
    "# 1.0 Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5dfff61",
   "metadata": {},
   "source": [
    "Hello everyone, welcome to my notebook for the Indian Startup Data Analysis Project. \n",
    "\n",
    "The objective of this project is to explore and find insights from the data on funding to Indian Startups from 2018 - 2021, and advice a team on their next steps as they try to venture into the Indian start-up ecosystem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d9b3b5",
   "metadata": {},
   "source": [
    "# 2.0 Ask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec9cdd1",
   "metadata": {},
   "source": [
    "### 2.1 Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22da519b",
   "metadata": {},
   "source": [
    " - H0: Funding to start-ups is centralized around a few locations and sectors\n",
    " - HA: Funding to start-ups is spread across different locations and sectors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b59b3d",
   "metadata": {},
   "source": [
    "### 2.2 Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752c8a31",
   "metadata": {},
   "source": [
    "To reach a final decision on whether or not to accept the hypothesis, the following questions will be answered:\n",
    "1.\tHow has funding to startups changed over the period?\n",
    "    -\tBy number of deals\n",
    "    -\tBy value of deals\n",
    "2.\tHow does location affect funding to startups?\n",
    "3.\tWhich sectors are most favoured by investors?\n",
    "    -\tBy number of deals\n",
    "    -\tBy amounts invested\n",
    "4.\tDoes the number of deals translate into funding for the sectors?\n",
    "5.\tWhat is the average amount of funding for start-ups in:\n",
    "    -\tThe sector with the most funding?\n",
    "    -\tThe location with the most funding?\n",
    "6.\tHow does the breakdown by stages of funding look?\n",
    "    -   By number of deals\n",
    "    -   By value of deals\n",
    "7.\tWhich start-ups were most favoured by investors?\n",
    "    -\tBy number of deals\n",
    "    -   By funding received"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0e7111",
   "metadata": {},
   "source": [
    "# 3.0 Prepare & Process (Data Cleaning & Preparation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb37254",
   "metadata": {},
   "source": [
    "### 3.1 Loading the libraries and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90ad124a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries and packages setup complete. Warnings hidden\n"
     ]
    }
   ],
   "source": [
    "# For data manipulation and cleaning\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# For data visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Other supporting libraries\n",
    "import re\n",
    "import warnings\n",
    "\n",
    "# Hiding the warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"Libraries and packages setup complete. Warnings hidden\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e19c9009",
   "metadata": {},
   "source": [
    "### 3.2 Loading the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "143d5cb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets loaded.\n"
     ]
    }
   ],
   "source": [
    "data_18 = pd.read_csv(\"startup_funding2018.csv\") # for the 2018 startup data\n",
    "data_19 =  pd.read_csv(\"startup_funding2019.csv\") # for the 2019 startup data\n",
    "data_20 = pd.read_csv(\"startup_funding2020.csv\") # for the 2020 startup data\n",
    "data_21 =  pd.read_csv(\"startup_funding2021.csv\") # for the 2021 startup data\n",
    "\n",
    "print(\"Datasets loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39956c0a",
   "metadata": {},
   "source": [
    "### 3.3 Previewing the Datasets & Getting Summary Information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418f3773",
   "metadata": {},
   "source": [
    "#### 3.3.1 The 2018 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e56a216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 526 entries, 0 to 525\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Company Name   526 non-null    object\n",
      " 1   Industry       526 non-null    object\n",
      " 2   Round/Series   526 non-null    object\n",
      " 3   Amount         526 non-null    object\n",
      " 4   Location       526 non-null    object\n",
      " 5   About Company  526 non-null    object\n",
      "dtypes: object(6)\n",
      "memory usage: 24.8+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Round/Series</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Location</th>\n",
       "      <th>About Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing, Event Promotion, Marketing, S...</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000</td>\n",
       "      <td>Bangalore, Karnataka, India</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture, Farming</td>\n",
       "      <td>Seed</td>\n",
       "      <td>₹40,000,000</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit, Financial Services, Lending, Marketplace</td>\n",
       "      <td>Series A</td>\n",
       "      <td>₹65,000,000</td>\n",
       "      <td>Gurgaon, Haryana, India</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services, FinTech</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000</td>\n",
       "      <td>Noida, Uttar Pradesh, India</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms, Retail, SaaS</td>\n",
       "      <td>Seed</td>\n",
       "      <td>—</td>\n",
       "      <td>Hyderabad, Andhra Pradesh, India</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Udaan</td>\n",
       "      <td>B2B, Business Development, Internet, Marketplace</td>\n",
       "      <td>Series C</td>\n",
       "      <td>225000000</td>\n",
       "      <td>Bangalore, Karnataka, India</td>\n",
       "      <td>Udaan is a B2B trade platform, designed specif...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Happyeasygo Group</td>\n",
       "      <td>Tourism, Travel</td>\n",
       "      <td>Series A</td>\n",
       "      <td>—</td>\n",
       "      <td>Haryana, Haryana, India</td>\n",
       "      <td>HappyEasyGo is an online travel domain.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Mombay</td>\n",
       "      <td>Food and Beverage, Food Delivery, Internet</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7500</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>Mombay is a unique opportunity for housewives ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Droni Tech</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Seed</td>\n",
       "      <td>₹35,000,000</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>Droni Tech manufacture UAVs and develop softwa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Netmeds</td>\n",
       "      <td>Biotechnology, Health Care, Pharmaceutical</td>\n",
       "      <td>Series C</td>\n",
       "      <td>35000000</td>\n",
       "      <td>Chennai, Tamil Nadu, India</td>\n",
       "      <td>Welcome to India's most convenient pharmacy!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name                                           Industry  \\\n",
       "0      TheCollegeFever  Brand Marketing, Event Promotion, Marketing, S...   \n",
       "1      Happy Cow Dairy                               Agriculture, Farming   \n",
       "2           MyLoanCare   Credit, Financial Services, Lending, Marketplace   \n",
       "3          PayMe India                        Financial Services, FinTech   \n",
       "4             Eunimart                 E-Commerce Platforms, Retail, SaaS   \n",
       "..                 ...                                                ...   \n",
       "521              Udaan   B2B, Business Development, Internet, Marketplace   \n",
       "522  Happyeasygo Group                                    Tourism, Travel   \n",
       "523             Mombay         Food and Beverage, Food Delivery, Internet   \n",
       "524         Droni Tech                             Information Technology   \n",
       "525            Netmeds         Biotechnology, Health Care, Pharmaceutical   \n",
       "\n",
       "    Round/Series       Amount                          Location  \\\n",
       "0           Seed       250000       Bangalore, Karnataka, India   \n",
       "1           Seed  ₹40,000,000        Mumbai, Maharashtra, India   \n",
       "2       Series A  ₹65,000,000           Gurgaon, Haryana, India   \n",
       "3          Angel      2000000       Noida, Uttar Pradesh, India   \n",
       "4           Seed            —  Hyderabad, Andhra Pradesh, India   \n",
       "..           ...          ...                               ...   \n",
       "521     Series C    225000000       Bangalore, Karnataka, India   \n",
       "522     Series A            —           Haryana, Haryana, India   \n",
       "523         Seed         7500        Mumbai, Maharashtra, India   \n",
       "524         Seed  ₹35,000,000        Mumbai, Maharashtra, India   \n",
       "525     Series C     35000000        Chennai, Tamil Nadu, India   \n",
       "\n",
       "                                         About Company  \n",
       "0    TheCollegeFever is a hub for fun, fiesta and f...  \n",
       "1    A startup which aggregates milk from dairy far...  \n",
       "2            Leading Online Loans Marketplace in India  \n",
       "3    PayMe India is an innovative FinTech organizat...  \n",
       "4    Eunimart is a one stop solution for merchants ...  \n",
       "..                                                 ...  \n",
       "521  Udaan is a B2B trade platform, designed specif...  \n",
       "522            HappyEasyGo is an online travel domain.  \n",
       "523  Mombay is a unique opportunity for housewives ...  \n",
       "524  Droni Tech manufacture UAVs and develop softwa...  \n",
       "525       Welcome to India's most convenient pharmacy!  \n",
       "\n",
       "[526 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_18.info(), \"\\n\")\n",
    "data_18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92212867",
   "metadata": {},
   "source": [
    "#### 3.3.2 The 2019 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ac8e5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 89 entries, 0 to 88\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company/Brand  89 non-null     object \n",
      " 1   Founded        60 non-null     float64\n",
      " 2   HeadQuarter    70 non-null     object \n",
      " 3   Sector         84 non-null     object \n",
      " 4   What it does   89 non-null     object \n",
      " 5   Founders       86 non-null     object \n",
      " 6   Investor       89 non-null     object \n",
      " 7   Amount($)      89 non-null     object \n",
      " 8   Stage          43 non-null     object \n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 6.4+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bombay Shaving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecommerce</td>\n",
       "      <td>Provides a range of male grooming products</td>\n",
       "      <td>Shantanu Deshpande</td>\n",
       "      <td>Sixth Sense Ventures</td>\n",
       "      <td>$6,300,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ruangguru</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>A learning platform that provides topic-based ...</td>\n",
       "      <td>Adamas Belva Syah Devara, Iman Usman.</td>\n",
       "      <td>General Atlantic</td>\n",
       "      <td>$150,000,000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eduisfun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>It aims to make learning fun via games.</td>\n",
       "      <td>Jatin Solanki</td>\n",
       "      <td>Deepak Parekh, Amitabh Bachchan, Piyush Pandey</td>\n",
       "      <td>$28,000,000</td>\n",
       "      <td>Fresh funding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HomeLane</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Interior design</td>\n",
       "      <td>Provides interior designing solutions</td>\n",
       "      <td>Srikanth Iyer, Rama Harinath</td>\n",
       "      <td>Evolvence India Fund (EIF), Pidilite Group, FJ...</td>\n",
       "      <td>$30,000,000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nu Genes</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>It is a seed company engaged in production, pr...</td>\n",
       "      <td>Narayana Reddy Punyala</td>\n",
       "      <td>Innovation in Food and Agriculture (IFA)</td>\n",
       "      <td>$6,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Infra.Market</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Infratech</td>\n",
       "      <td>It connects client requirements to their suppl...</td>\n",
       "      <td>Aaditya Sharda, Souvik Sengupta</td>\n",
       "      <td>Tiger Global, Nexus Venture Partners, Accel Pa...</td>\n",
       "      <td>$20,000,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Oyo</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Hospitality</td>\n",
       "      <td>Provides rooms for comfortable stay</td>\n",
       "      <td>Ritesh Agarwal</td>\n",
       "      <td>MyPreferred Transformation, Avendus Finance, S...</td>\n",
       "      <td>$693,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>GoMechanic</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Automobile &amp; Technology</td>\n",
       "      <td>Find automobile repair and maintenance service...</td>\n",
       "      <td>Amit Bhasin, Kushal Karwa, Nitin Rana, Rishabh...</td>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>$5,000,000</td>\n",
       "      <td>Series B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Spinny</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Online car retailer</td>\n",
       "      <td>Niraj Singh, Ramanshu Mahaur, Ganesh Pawar, Mo...</td>\n",
       "      <td>Norwest Venture Partners, General Catalyst, Fu...</td>\n",
       "      <td>$50,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Ess Kay Fincorp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Banking</td>\n",
       "      <td>Organised Non-Banking Finance Company</td>\n",
       "      <td>Rajendra Setia</td>\n",
       "      <td>TPG, Norwest Venture Partners, Evolvence India</td>\n",
       "      <td>$33,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company/Brand  Founded HeadQuarter                   Sector  \\\n",
       "0    Bombay Shaving      NaN         NaN                Ecommerce   \n",
       "1         Ruangguru   2014.0      Mumbai                   Edtech   \n",
       "2          Eduisfun      NaN      Mumbai                   Edtech   \n",
       "3          HomeLane   2014.0     Chennai          Interior design   \n",
       "4          Nu Genes   2004.0   Telangana                 AgriTech   \n",
       "..              ...      ...         ...                      ...   \n",
       "84     Infra.Market      NaN      Mumbai                Infratech   \n",
       "85              Oyo   2013.0    Gurugram              Hospitality   \n",
       "86       GoMechanic   2016.0       Delhi  Automobile & Technology   \n",
       "87           Spinny   2015.0       Delhi               Automobile   \n",
       "88  Ess Kay Fincorp      NaN   Rajasthan                  Banking   \n",
       "\n",
       "                                         What it does  \\\n",
       "0          Provides a range of male grooming products   \n",
       "1   A learning platform that provides topic-based ...   \n",
       "2             It aims to make learning fun via games.   \n",
       "3               Provides interior designing solutions   \n",
       "4   It is a seed company engaged in production, pr...   \n",
       "..                                                ...   \n",
       "84  It connects client requirements to their suppl...   \n",
       "85                Provides rooms for comfortable stay   \n",
       "86  Find automobile repair and maintenance service...   \n",
       "87                                Online car retailer   \n",
       "88              Organised Non-Banking Finance Company   \n",
       "\n",
       "                                             Founders  \\\n",
       "0                                  Shantanu Deshpande   \n",
       "1               Adamas Belva Syah Devara, Iman Usman.   \n",
       "2                                       Jatin Solanki   \n",
       "3                        Srikanth Iyer, Rama Harinath   \n",
       "4                              Narayana Reddy Punyala   \n",
       "..                                                ...   \n",
       "84                    Aaditya Sharda, Souvik Sengupta   \n",
       "85                                     Ritesh Agarwal   \n",
       "86  Amit Bhasin, Kushal Karwa, Nitin Rana, Rishabh...   \n",
       "87  Niraj Singh, Ramanshu Mahaur, Ganesh Pawar, Mo...   \n",
       "88                                     Rajendra Setia   \n",
       "\n",
       "                                             Investor     Amount($)  \\\n",
       "0                                Sixth Sense Ventures    $6,300,000   \n",
       "1                                    General Atlantic  $150,000,000   \n",
       "2      Deepak Parekh, Amitabh Bachchan, Piyush Pandey   $28,000,000   \n",
       "3   Evolvence India Fund (EIF), Pidilite Group, FJ...   $30,000,000   \n",
       "4            Innovation in Food and Agriculture (IFA)    $6,000,000   \n",
       "..                                                ...           ...   \n",
       "84  Tiger Global, Nexus Venture Partners, Accel Pa...   $20,000,000   \n",
       "85  MyPreferred Transformation, Avendus Finance, S...  $693,000,000   \n",
       "86                                    Sequoia Capital    $5,000,000   \n",
       "87  Norwest Venture Partners, General Catalyst, Fu...   $50,000,000   \n",
       "88     TPG, Norwest Venture Partners, Evolvence India   $33,000,000   \n",
       "\n",
       "            Stage  \n",
       "0             NaN  \n",
       "1        Series C  \n",
       "2   Fresh funding  \n",
       "3        Series D  \n",
       "4             NaN  \n",
       "..            ...  \n",
       "84       Series A  \n",
       "85            NaN  \n",
       "86       Series B  \n",
       "87            NaN  \n",
       "88            NaN  \n",
       "\n",
       "[89 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_19.info(), \"\\n\")\n",
    "data_19"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8eef4c3",
   "metadata": {},
   "source": [
    "#### 3.3.2 The 2020 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83f14838",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1055 entries, 0 to 1054\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Company/Brand  1055 non-null   object\n",
      " 1   Founded        843 non-null    object\n",
      " 2   HeadQuarter    961 non-null    object\n",
      " 3   Sector         1042 non-null   object\n",
      " 4   What it does   1055 non-null   object\n",
      " 5   Founders       1043 non-null   object\n",
      " 6   Investor       1017 non-null   object\n",
      " 7   Amount($)      1052 non-null   object\n",
      " 8   Stage          591 non-null    object\n",
      " 9   Unnamed: 9     2 non-null      object\n",
      "dtypes: object(10)\n",
      "memory usage: 82.5+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aqgromalin</td>\n",
       "      <td>2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>Cultivating Ideas for Profit</td>\n",
       "      <td>Prasanna Manogaran, Bharani C L</td>\n",
       "      <td>Angel investors</td>\n",
       "      <td>$200,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Krayonnz</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>An academy-guardian-scholar centric ecosystem ...</td>\n",
       "      <td>Saurabh Dixit, Gurudutt Upadhyay</td>\n",
       "      <td>GSF Accelerator</td>\n",
       "      <td>$100,000</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PadCare Labs</td>\n",
       "      <td>2018</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Hygiene management</td>\n",
       "      <td>Converting bio-hazardous waste to harmless waste</td>\n",
       "      <td>Ajinkya Dhariya</td>\n",
       "      <td>Venture Center</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCOME</td>\n",
       "      <td>2020</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Escrow</td>\n",
       "      <td>Escrow-as-a-service platform</td>\n",
       "      <td>Ritesh Tiwari</td>\n",
       "      <td>Venture Catalysts, PointOne Capital</td>\n",
       "      <td>$400,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gramophone</td>\n",
       "      <td>2016</td>\n",
       "      <td>Indore</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>Gramophone is an AgTech platform enabling acce...</td>\n",
       "      <td>Ashish Rajan Singh, Harshit Gupta, Nishant Mah...</td>\n",
       "      <td>Siana Capital Management, Info Edge</td>\n",
       "      <td>$340,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>Leverage Edu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>AI enabled marketplace that provides career gu...</td>\n",
       "      <td>Akshay Chaturvedi</td>\n",
       "      <td>DSG Consumer Partners, Blume Ventures</td>\n",
       "      <td>$1,500,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>EpiFi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>It offers customers with a single interface fo...</td>\n",
       "      <td>Sujith Narayanan, Sumit Gwalani</td>\n",
       "      <td>Sequoia India, Ribbit Capital</td>\n",
       "      <td>$13,200,000</td>\n",
       "      <td>Seed Round</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>Purplle</td>\n",
       "      <td>2012</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Cosmetics</td>\n",
       "      <td>Online makeup and beauty products retailer</td>\n",
       "      <td>Manish Taneja, Rahul Dash</td>\n",
       "      <td>Verlinvest</td>\n",
       "      <td>$8,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>Shuttl</td>\n",
       "      <td>2015</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Transport</td>\n",
       "      <td>App based bus aggregator serice</td>\n",
       "      <td>Amit Singh, Deepanshu Malviya</td>\n",
       "      <td>SIG Global India Fund LLP.</td>\n",
       "      <td>$8,043,000</td>\n",
       "      <td>Series C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>Pando</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Logitech</td>\n",
       "      <td>Networked logistics management software</td>\n",
       "      <td>Jayakrishnan, Abhijeet Manohar</td>\n",
       "      <td>Chiratae Ventures</td>\n",
       "      <td>$9,000,000</td>\n",
       "      <td>Series A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1055 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company/Brand Founded HeadQuarter              Sector  \\\n",
       "0       Aqgromalin    2019     Chennai            AgriTech   \n",
       "1         Krayonnz    2019   Bangalore              EdTech   \n",
       "2     PadCare Labs    2018        Pune  Hygiene management   \n",
       "3            NCOME    2020   New Delhi              Escrow   \n",
       "4       Gramophone    2016      Indore            AgriTech   \n",
       "...            ...     ...         ...                 ...   \n",
       "1050  Leverage Edu     NaN       Delhi              Edtech   \n",
       "1051         EpiFi     NaN         NaN             Fintech   \n",
       "1052       Purplle    2012      Mumbai           Cosmetics   \n",
       "1053        Shuttl    2015       Delhi           Transport   \n",
       "1054         Pando    2017     Chennai            Logitech   \n",
       "\n",
       "                                           What it does  \\\n",
       "0                          Cultivating Ideas for Profit   \n",
       "1     An academy-guardian-scholar centric ecosystem ...   \n",
       "2      Converting bio-hazardous waste to harmless waste   \n",
       "3                          Escrow-as-a-service platform   \n",
       "4     Gramophone is an AgTech platform enabling acce...   \n",
       "...                                                 ...   \n",
       "1050  AI enabled marketplace that provides career gu...   \n",
       "1051  It offers customers with a single interface fo...   \n",
       "1052         Online makeup and beauty products retailer   \n",
       "1053                    App based bus aggregator serice   \n",
       "1054            Networked logistics management software   \n",
       "\n",
       "                                               Founders  \\\n",
       "0                       Prasanna Manogaran, Bharani C L   \n",
       "1                      Saurabh Dixit, Gurudutt Upadhyay   \n",
       "2                                       Ajinkya Dhariya   \n",
       "3                                         Ritesh Tiwari   \n",
       "4     Ashish Rajan Singh, Harshit Gupta, Nishant Mah...   \n",
       "...                                                 ...   \n",
       "1050                                  Akshay Chaturvedi   \n",
       "1051                    Sujith Narayanan, Sumit Gwalani   \n",
       "1052                          Manish Taneja, Rahul Dash   \n",
       "1053                      Amit Singh, Deepanshu Malviya   \n",
       "1054                     Jayakrishnan, Abhijeet Manohar   \n",
       "\n",
       "                                   Investor    Amount($)       Stage  \\\n",
       "0                           Angel investors     $200,000         NaN   \n",
       "1                           GSF Accelerator     $100,000    Pre-seed   \n",
       "2                            Venture Center  Undisclosed    Pre-seed   \n",
       "3       Venture Catalysts, PointOne Capital     $400,000         NaN   \n",
       "4       Siana Capital Management, Info Edge     $340,000         NaN   \n",
       "...                                     ...          ...         ...   \n",
       "1050  DSG Consumer Partners, Blume Ventures   $1,500,000         NaN   \n",
       "1051          Sequoia India, Ribbit Capital  $13,200,000  Seed Round   \n",
       "1052                             Verlinvest   $8,000,000         NaN   \n",
       "1053             SIG Global India Fund LLP.   $8,043,000    Series C   \n",
       "1054                      Chiratae Ventures   $9,000,000    Series A   \n",
       "\n",
       "     Unnamed: 9  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "1050        NaN  \n",
       "1051        NaN  \n",
       "1052        NaN  \n",
       "1053        NaN  \n",
       "1054        NaN  \n",
       "\n",
       "[1055 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_20.info(), \"\\n\")\n",
    "data_20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e781e426",
   "metadata": {},
   "source": [
    "#### 3.3.2 The 2021 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fa8f794",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1209 entries, 0 to 1208\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company/Brand  1209 non-null   object \n",
      " 1   Founded        1208 non-null   float64\n",
      " 2   HeadQuarter    1208 non-null   object \n",
      " 3   Sector         1209 non-null   object \n",
      " 4   What it does   1209 non-null   object \n",
      " 5   Founders       1205 non-null   object \n",
      " 6   Investor       1147 non-null   object \n",
      " 7   Amount($)      1206 non-null   object \n",
      " 8   Stage          781 non-null    object \n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 85.1+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unbox Robotics</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>AI startup</td>\n",
       "      <td>Unbox Robotics builds on-demand AI-driven ware...</td>\n",
       "      <td>Pramod Ghadge, Shahid Memon</td>\n",
       "      <td>BEENEXT, Entrepreneur First</td>\n",
       "      <td>$1,200,000</td>\n",
       "      <td>Pre-series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>upGrad</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>UpGrad is an online higher education platform.</td>\n",
       "      <td>Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...</td>\n",
       "      <td>Unilazer Ventures, IIFL Asset Management</td>\n",
       "      <td>$120,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lead School</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>LEAD School offers technology based school tra...</td>\n",
       "      <td>Smita Deorah, Sumeet Mehta</td>\n",
       "      <td>GSV Ventures, Westbridge Capital</td>\n",
       "      <td>$30,000,000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bizongo</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>B2B E-commerce</td>\n",
       "      <td>Bizongo is a business-to-business online marke...</td>\n",
       "      <td>Aniket Deb, Ankit Tomar, Sachin Agrawal</td>\n",
       "      <td>CDC Group, IDG Capital</td>\n",
       "      <td>$51,000,000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FypMoney</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>FypMoney is Digital NEO Bank for Teenagers, em...</td>\n",
       "      <td>Kapil Banwari</td>\n",
       "      <td>Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal</td>\n",
       "      <td>$2,000,000</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>Gigforce</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Staffing &amp; Recruiting</td>\n",
       "      <td>A gig/on-demand staffing company.</td>\n",
       "      <td>Chirag Mittal, Anirudh Syal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "      <td>$3000000</td>\n",
       "      <td>Pre-series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>Vahdam</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Food &amp; Beverages</td>\n",
       "      <td>VAHDAM is among the world’s first vertically i...</td>\n",
       "      <td>Bala Sarda</td>\n",
       "      <td>IIFL AMC</td>\n",
       "      <td>$20000000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>Leap Finance</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>International education loans for high potenti...</td>\n",
       "      <td>Arnav Kumar, Vaibhav Singh</td>\n",
       "      <td>Owl Ventures</td>\n",
       "      <td>$55000000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>CollegeDekho</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Collegedekho.com is Student’s Partner, Friend ...</td>\n",
       "      <td>Ruchir Arora</td>\n",
       "      <td>Winter Capital, ETS, Man Capital</td>\n",
       "      <td>$26000000</td>\n",
       "      <td>Series B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>WeRize</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>Vishal Chopra, Himanshu Gupta</td>\n",
       "      <td>3one4 Capital, Kalaari Capital</td>\n",
       "      <td>$8000000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1209 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company/Brand  Founded HeadQuarter                 Sector  \\\n",
       "0     Unbox Robotics   2019.0   Bangalore             AI startup   \n",
       "1             upGrad   2015.0      Mumbai                 EdTech   \n",
       "2        Lead School   2012.0      Mumbai                 EdTech   \n",
       "3            Bizongo   2015.0      Mumbai         B2B E-commerce   \n",
       "4           FypMoney   2021.0    Gurugram                FinTech   \n",
       "...              ...      ...         ...                    ...   \n",
       "1204        Gigforce   2019.0    Gurugram  Staffing & Recruiting   \n",
       "1205          Vahdam   2015.0   New Delhi       Food & Beverages   \n",
       "1206    Leap Finance   2019.0   Bangalore     Financial Services   \n",
       "1207    CollegeDekho   2015.0    Gurugram                 EdTech   \n",
       "1208          WeRize   2019.0   Bangalore     Financial Services   \n",
       "\n",
       "                                           What it does  \\\n",
       "0     Unbox Robotics builds on-demand AI-driven ware...   \n",
       "1        UpGrad is an online higher education platform.   \n",
       "2     LEAD School offers technology based school tra...   \n",
       "3     Bizongo is a business-to-business online marke...   \n",
       "4     FypMoney is Digital NEO Bank for Teenagers, em...   \n",
       "...                                                 ...   \n",
       "1204                  A gig/on-demand staffing company.   \n",
       "1205  VAHDAM is among the world’s first vertically i...   \n",
       "1206  International education loans for high potenti...   \n",
       "1207  Collegedekho.com is Student’s Partner, Friend ...   \n",
       "1208  India’s first socially distributed full stack ...   \n",
       "\n",
       "                                               Founders  \\\n",
       "0                           Pramod Ghadge, Shahid Memon   \n",
       "1     Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...   \n",
       "2                            Smita Deorah, Sumeet Mehta   \n",
       "3               Aniket Deb, Ankit Tomar, Sachin Agrawal   \n",
       "4                                         Kapil Banwari   \n",
       "...                                                 ...   \n",
       "1204                        Chirag Mittal, Anirudh Syal   \n",
       "1205                                         Bala Sarda   \n",
       "1206                         Arnav Kumar, Vaibhav Singh   \n",
       "1207                                       Ruchir Arora   \n",
       "1208                      Vishal Chopra, Himanshu Gupta   \n",
       "\n",
       "                                           Investor     Amount($)  \\\n",
       "0                       BEENEXT, Entrepreneur First    $1,200,000   \n",
       "1          Unilazer Ventures, IIFL Asset Management  $120,000,000   \n",
       "2                  GSV Ventures, Westbridge Capital   $30,000,000   \n",
       "3                            CDC Group, IDG Capital   $51,000,000   \n",
       "4     Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal    $2,000,000   \n",
       "...                                             ...           ...   \n",
       "1204                                Endiya Partners      $3000000   \n",
       "1205                                       IIFL AMC     $20000000   \n",
       "1206                                   Owl Ventures     $55000000   \n",
       "1207               Winter Capital, ETS, Man Capital     $26000000   \n",
       "1208                 3one4 Capital, Kalaari Capital      $8000000   \n",
       "\n",
       "             Stage  \n",
       "0     Pre-series A  \n",
       "1              NaN  \n",
       "2         Series D  \n",
       "3         Series C  \n",
       "4             Seed  \n",
       "...            ...  \n",
       "1204  Pre-series A  \n",
       "1205      Series D  \n",
       "1206      Series C  \n",
       "1207      Series B  \n",
       "1208      Series A  \n",
       "\n",
       "[1209 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_21.info(), \"\\n\")\n",
    "data_21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baaddf6e",
   "metadata": {},
   "source": [
    "### 3.4 Observations from previewing the datasets\n",
    "**3.4.1 The 2018 DataFrame**\n",
    "- The columns in 2018 are different from those of 2019 - 2021, meaning they have to be renamed for concatenation.\n",
    "- The amounts in the 2018 DataFrame are a mix of Indian Rupees (INR) and US Dollars (USD), meaning they have to be converted into same currency.\n",
    "- The industry and location columns have multiple information. A decision is to be made between selecting the first value before the separator(,) as the main value, or representing that column with a wordcloud.\n",
    "\n",
    "**3.4.2 The 2019 DataFrame**\n",
    "- The datatype of the \"Founded\" column is set to float64. It should be set to a string for uniformity.\n",
    "- The headquarter column has multiple information. A decision is to be made between selecting the first value before the separator(,) as the main value, or representing that column with a wordcloud.\n",
    "\n",
    "**3.4.3 The 2020 DataFrame**\n",
    "- There is an extra column called \"Unnamed:9\", giving it a total of 10 columns. It should be dropped to ensure complete alignment with the other DataFrames for ease of concatenation.\n",
    "\n",
    "**3.4.4 The 2021 DataFrame**\n",
    "- The datatype of the \"Founded\" column is set to float64. It should be set to a string for uniformity.\n",
    "\n",
    "**3.4.5 General Observations**\n",
    "- The currency signs and commas have to be removed from each of amount column for each DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee1e870",
   "metadata": {},
   "source": [
    "### 3.5 Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6546c14d",
   "metadata": {},
   "source": [
    "- The 2018 average INR/USD rate will be used to convert the Indian Rupee values to US Dollars in the 2018 DataFrame.\n",
    "- First values of industry and location in the 2018 data will be selected as the primary sector and headquarters respectively.\n",
    "- Amounts without currency symbols are assumed to be in USD ($)\n",
    "- Financial analysis will be narrowed to transactions whose amounts are available in the loaded data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38742c80",
   "metadata": {},
   "source": [
    "### 3.6 Acting on the Observations and Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ec0061",
   "metadata": {},
   "source": [
    "#### 3.6.1 Processing the 2018 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "104b9edc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Round/Series</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Location</th>\n",
       "      <th>About Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing, Event Promotion, Marketing, S...</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000</td>\n",
       "      <td>Bangalore, Karnataka, India</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture, Farming</td>\n",
       "      <td>Seed</td>\n",
       "      <td>₹40,000,000</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit, Financial Services, Lending, Marketplace</td>\n",
       "      <td>Series A</td>\n",
       "      <td>₹65,000,000</td>\n",
       "      <td>Gurgaon, Haryana, India</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services, FinTech</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000</td>\n",
       "      <td>Noida, Uttar Pradesh, India</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms, Retail, SaaS</td>\n",
       "      <td>Seed</td>\n",
       "      <td>—</td>\n",
       "      <td>Hyderabad, Andhra Pradesh, India</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Udaan</td>\n",
       "      <td>B2B, Business Development, Internet, Marketplace</td>\n",
       "      <td>Series C</td>\n",
       "      <td>225000000</td>\n",
       "      <td>Bangalore, Karnataka, India</td>\n",
       "      <td>Udaan is a B2B trade platform, designed specif...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Happyeasygo Group</td>\n",
       "      <td>Tourism, Travel</td>\n",
       "      <td>Series A</td>\n",
       "      <td>—</td>\n",
       "      <td>Haryana, Haryana, India</td>\n",
       "      <td>HappyEasyGo is an online travel domain.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Mombay</td>\n",
       "      <td>Food and Beverage, Food Delivery, Internet</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7500</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>Mombay is a unique opportunity for housewives ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Droni Tech</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Seed</td>\n",
       "      <td>₹35,000,000</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>Droni Tech manufacture UAVs and develop softwa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Netmeds</td>\n",
       "      <td>Biotechnology, Health Care, Pharmaceutical</td>\n",
       "      <td>Series C</td>\n",
       "      <td>35000000</td>\n",
       "      <td>Chennai, Tamil Nadu, India</td>\n",
       "      <td>Welcome to India's most convenient pharmacy!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name                                           Industry  \\\n",
       "0      TheCollegeFever  Brand Marketing, Event Promotion, Marketing, S...   \n",
       "1      Happy Cow Dairy                               Agriculture, Farming   \n",
       "2           MyLoanCare   Credit, Financial Services, Lending, Marketplace   \n",
       "3          PayMe India                        Financial Services, FinTech   \n",
       "4             Eunimart                 E-Commerce Platforms, Retail, SaaS   \n",
       "..                 ...                                                ...   \n",
       "521              Udaan   B2B, Business Development, Internet, Marketplace   \n",
       "522  Happyeasygo Group                                    Tourism, Travel   \n",
       "523             Mombay         Food and Beverage, Food Delivery, Internet   \n",
       "524         Droni Tech                             Information Technology   \n",
       "525            Netmeds         Biotechnology, Health Care, Pharmaceutical   \n",
       "\n",
       "    Round/Series       Amount                          Location  \\\n",
       "0           Seed       250000       Bangalore, Karnataka, India   \n",
       "1           Seed  ₹40,000,000        Mumbai, Maharashtra, India   \n",
       "2       Series A  ₹65,000,000           Gurgaon, Haryana, India   \n",
       "3          Angel      2000000       Noida, Uttar Pradesh, India   \n",
       "4           Seed            —  Hyderabad, Andhra Pradesh, India   \n",
       "..           ...          ...                               ...   \n",
       "521     Series C    225000000       Bangalore, Karnataka, India   \n",
       "522     Series A            —           Haryana, Haryana, India   \n",
       "523         Seed         7500        Mumbai, Maharashtra, India   \n",
       "524         Seed  ₹35,000,000        Mumbai, Maharashtra, India   \n",
       "525     Series C     35000000        Chennai, Tamil Nadu, India   \n",
       "\n",
       "                                         About Company  \n",
       "0    TheCollegeFever is a hub for fun, fiesta and f...  \n",
       "1    A startup which aggregates milk from dairy far...  \n",
       "2            Leading Online Loans Marketplace in India  \n",
       "3    PayMe India is an innovative FinTech organizat...  \n",
       "4    Eunimart is a one stop solution for merchants ...  \n",
       "..                                                 ...  \n",
       "521  Udaan is a B2B trade platform, designed specif...  \n",
       "522            HappyEasyGo is an online travel domain.  \n",
       "523  Mombay is a unique opportunity for housewives ...  \n",
       "524  Droni Tech manufacture UAVs and develop softwa...  \n",
       "525       Welcome to India's most convenient pharmacy!  \n",
       "\n",
       "[526 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f700bfe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Round/Series</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Location</th>\n",
       "      <th>About Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Seed</td>\n",
       "      <td>₹40,000,000</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit</td>\n",
       "      <td>Series A</td>\n",
       "      <td>₹65,000,000</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms</td>\n",
       "      <td>Seed</td>\n",
       "      <td>—</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Udaan</td>\n",
       "      <td>B2B</td>\n",
       "      <td>Series C</td>\n",
       "      <td>225000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Udaan is a B2B trade platform, designed specif...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Happyeasygo Group</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>Series A</td>\n",
       "      <td>—</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>HappyEasyGo is an online travel domain.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Mombay</td>\n",
       "      <td>Food and Beverage</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7500</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mombay is a unique opportunity for housewives ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Droni Tech</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Seed</td>\n",
       "      <td>₹35,000,000</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Droni Tech manufacture UAVs and develop softwa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Netmeds</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>Series C</td>\n",
       "      <td>35000000</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Welcome to India's most convenient pharmacy!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name                Industry Round/Series       Amount  \\\n",
       "0      TheCollegeFever         Brand Marketing         Seed       250000   \n",
       "1      Happy Cow Dairy             Agriculture         Seed  ₹40,000,000   \n",
       "2           MyLoanCare                  Credit     Series A  ₹65,000,000   \n",
       "3          PayMe India      Financial Services        Angel      2000000   \n",
       "4             Eunimart    E-Commerce Platforms         Seed            —   \n",
       "..                 ...                     ...          ...          ...   \n",
       "521              Udaan                     B2B     Series C    225000000   \n",
       "522  Happyeasygo Group                 Tourism     Series A            —   \n",
       "523             Mombay       Food and Beverage         Seed         7500   \n",
       "524         Droni Tech  Information Technology         Seed  ₹35,000,000   \n",
       "525            Netmeds           Biotechnology     Series C     35000000   \n",
       "\n",
       "      Location                                      About Company  \n",
       "0    Bangalore  TheCollegeFever is a hub for fun, fiesta and f...  \n",
       "1       Mumbai  A startup which aggregates milk from dairy far...  \n",
       "2      Gurgaon          Leading Online Loans Marketplace in India  \n",
       "3        Noida  PayMe India is an innovative FinTech organizat...  \n",
       "4    Hyderabad  Eunimart is a one stop solution for merchants ...  \n",
       "..         ...                                                ...  \n",
       "521  Bangalore  Udaan is a B2B trade platform, designed specif...  \n",
       "522    Haryana            HappyEasyGo is an online travel domain.  \n",
       "523     Mumbai  Mombay is a unique opportunity for housewives ...  \n",
       "524     Mumbai  Droni Tech manufacture UAVs and develop softwa...  \n",
       "525    Chennai       Welcome to India's most convenient pharmacy!  \n",
       "\n",
       "[526 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting the main industries of the startups as Industry\n",
    "data_18['Industry'] = data_18['Industry'].apply(str)\n",
    "data_18['Industry'] = data_18['Industry'].str.split(',').str[0]\n",
    "data_18['Industry'] = data_18['Industry'].replace(\"'\", \"\", regex=True)\n",
    "\n",
    "# Selecting the main locations of the startups as Location\n",
    "data_18['Location'] = data_18['Location'].apply(str)\n",
    "data_18['Location'] = data_18['Location'].str.split(',').str[0]\n",
    "data_18['Location'] = data_18['Location'].replace(\"'\", \"\", regex=True)\n",
    "\n",
    "data_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae9541ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 526 entries, 0 to 525\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Company Name     526 non-null    object \n",
      " 1   Industry         526 non-null    object \n",
      " 2   Round/Series     526 non-null    object \n",
      " 3   Amount           378 non-null    float64\n",
      " 4   Location         526 non-null    object \n",
      " 5   About Company    526 non-null    object \n",
      " 6   Year of Funding  526 non-null    object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 28.9+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Round/Series</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Location</th>\n",
       "      <th>About Company</th>\n",
       "      <th>Year of Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Seed</td>\n",
       "      <td>584000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit</td>\n",
       "      <td>Series A</td>\n",
       "      <td>949000.0</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Udaan</td>\n",
       "      <td>B2B</td>\n",
       "      <td>Series C</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Udaan is a B2B trade platform, designed specif...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Happyeasygo Group</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>Series A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>HappyEasyGo is an online travel domain.</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Mombay</td>\n",
       "      <td>Food and Beverage</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mombay is a unique opportunity for housewives ...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Droni Tech</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Seed</td>\n",
       "      <td>511000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Droni Tech manufacture UAVs and develop softwa...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Netmeds</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>Series C</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Welcome to India's most convenient pharmacy!</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name                Industry Round/Series       Amount  \\\n",
       "0      TheCollegeFever         Brand Marketing         Seed     250000.0   \n",
       "1      Happy Cow Dairy             Agriculture         Seed     584000.0   \n",
       "2           MyLoanCare                  Credit     Series A     949000.0   \n",
       "3          PayMe India      Financial Services        Angel    2000000.0   \n",
       "4             Eunimart    E-Commerce Platforms         Seed          NaN   \n",
       "..                 ...                     ...          ...          ...   \n",
       "521              Udaan                     B2B     Series C  225000000.0   \n",
       "522  Happyeasygo Group                 Tourism     Series A          NaN   \n",
       "523             Mombay       Food and Beverage         Seed       7500.0   \n",
       "524         Droni Tech  Information Technology         Seed     511000.0   \n",
       "525            Netmeds           Biotechnology     Series C   35000000.0   \n",
       "\n",
       "      Location                                      About Company  \\\n",
       "0    Bangalore  TheCollegeFever is a hub for fun, fiesta and f...   \n",
       "1       Mumbai  A startup which aggregates milk from dairy far...   \n",
       "2      Gurgaon          Leading Online Loans Marketplace in India   \n",
       "3        Noida  PayMe India is an innovative FinTech organizat...   \n",
       "4    Hyderabad  Eunimart is a one stop solution for merchants ...   \n",
       "..         ...                                                ...   \n",
       "521  Bangalore  Udaan is a B2B trade platform, designed specif...   \n",
       "522    Haryana            HappyEasyGo is an online travel domain.   \n",
       "523     Mumbai  Mombay is a unique opportunity for housewives ...   \n",
       "524     Mumbai  Droni Tech manufacture UAVs and develop softwa...   \n",
       "525    Chennai       Welcome to India's most convenient pharmacy!   \n",
       "\n",
       "    Year of Funding  \n",
       "0              2018  \n",
       "1              2018  \n",
       "2              2018  \n",
       "3              2018  \n",
       "4              2018  \n",
       "..              ...  \n",
       "521            2018  \n",
       "522            2018  \n",
       "523            2018  \n",
       "524            2018  \n",
       "525            2018  \n",
       "\n",
       "[526 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning the Amounts column\n",
    "## Removing the commas and dashes from the Amounts\n",
    "data_18['Amount'] = data_18['Amount'].apply(str)\n",
    "data_18['Amount'].replace(\",\", \"\", inplace = True, regex=True)\n",
    "data_18['Amount'].replace(\"—\", 0, inplace = True, regex=True)\n",
    "data_18['Amount'].replace(\"$\", \"\", inplace = True, regex=True)\n",
    "\n",
    "## Creating temporary columns to help with the conversion of INR to USD\n",
    "data_18['INR Amount'] = data_18['Amount'].str.rsplit('₹', n = 2).str[1]\n",
    "data_18['INR Amount'] = data_18['INR Amount'].apply(float).fillna(0)\n",
    "data_18['USD Amount'] = data_18['INR Amount'] * 0.0146\n",
    "data_18['USD Amount'] = data_18['USD Amount'].replace(0, np.nan)\n",
    "data_18['USD Amount'] = data_18['USD Amount'].fillna(data_18['Amount'])\n",
    "data_18['USD Amount'] = data_18['USD Amount'].replace(\"$\", \"\", regex=True)\n",
    "data_18[\"Amount\"] = data_18[\"USD Amount\"]\n",
    "data_18[\"Amount\"] = data_18[\"Amount\"].apply(lambda x: float(str(x).replace(\"$\",\"\")))\n",
    "data_18[\"Amount\"] = data_18[\"Amount\"].replace(0, np.nan)\n",
    "\n",
    "# Dropping the temporary columns\n",
    "data_18.drop(columns = [\"INR Amount\", \"USD Amount\"], inplace = True)\n",
    "\n",
    "# Correcting the mistaken Funding Round entry\n",
    "data_18['Round/Series'] = data_18['Round/Series'].replace(\"https://docs.google.com/spreadsheets/d/1x9ziNeaz6auNChIHnMI8U6kS7knTr3byy_YBGfQaoUA/edit#gid=1861303593\", \"Seed\")\n",
    "\n",
    "# Adding a column to represent the year of funding\n",
    "data_18[\"Year of Funding\"] = \"2018\"\n",
    "\n",
    "print(data_18.info(), \"\\n\")\n",
    "data_18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03929eb",
   "metadata": {},
   "source": [
    "#### 3.6.2 Processing the 2019 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b8caa17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 89 entries, 0 to 88\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company/Brand  89 non-null     object \n",
      " 1   Founded        60 non-null     float64\n",
      " 2   HeadQuarter    70 non-null     object \n",
      " 3   Sector         84 non-null     object \n",
      " 4   What it does   89 non-null     object \n",
      " 5   Founders       86 non-null     object \n",
      " 6   Investor       89 non-null     object \n",
      " 7   Amount($)      89 non-null     object \n",
      " 8   Stage          43 non-null     object \n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 6.4+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bombay Shaving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecommerce</td>\n",
       "      <td>Provides a range of male grooming products</td>\n",
       "      <td>Shantanu Deshpande</td>\n",
       "      <td>Sixth Sense Ventures</td>\n",
       "      <td>$6,300,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ruangguru</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>A learning platform that provides topic-based ...</td>\n",
       "      <td>Adamas Belva Syah Devara, Iman Usman.</td>\n",
       "      <td>General Atlantic</td>\n",
       "      <td>$150,000,000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eduisfun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>It aims to make learning fun via games.</td>\n",
       "      <td>Jatin Solanki</td>\n",
       "      <td>Deepak Parekh, Amitabh Bachchan, Piyush Pandey</td>\n",
       "      <td>$28,000,000</td>\n",
       "      <td>Fresh funding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HomeLane</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Interior design</td>\n",
       "      <td>Provides interior designing solutions</td>\n",
       "      <td>Srikanth Iyer, Rama Harinath</td>\n",
       "      <td>Evolvence India Fund (EIF), Pidilite Group, FJ...</td>\n",
       "      <td>$30,000,000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nu Genes</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>It is a seed company engaged in production, pr...</td>\n",
       "      <td>Narayana Reddy Punyala</td>\n",
       "      <td>Innovation in Food and Agriculture (IFA)</td>\n",
       "      <td>$6,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Infra.Market</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Infratech</td>\n",
       "      <td>It connects client requirements to their suppl...</td>\n",
       "      <td>Aaditya Sharda, Souvik Sengupta</td>\n",
       "      <td>Tiger Global, Nexus Venture Partners, Accel Pa...</td>\n",
       "      <td>$20,000,000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Oyo</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Hospitality</td>\n",
       "      <td>Provides rooms for comfortable stay</td>\n",
       "      <td>Ritesh Agarwal</td>\n",
       "      <td>MyPreferred Transformation, Avendus Finance, S...</td>\n",
       "      <td>$693,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>GoMechanic</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Automobile &amp; Technology</td>\n",
       "      <td>Find automobile repair and maintenance service...</td>\n",
       "      <td>Amit Bhasin, Kushal Karwa, Nitin Rana, Rishabh...</td>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>$5,000,000</td>\n",
       "      <td>Series B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Spinny</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Online car retailer</td>\n",
       "      <td>Niraj Singh, Ramanshu Mahaur, Ganesh Pawar, Mo...</td>\n",
       "      <td>Norwest Venture Partners, General Catalyst, Fu...</td>\n",
       "      <td>$50,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Ess Kay Fincorp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Banking</td>\n",
       "      <td>Organised Non-Banking Finance Company</td>\n",
       "      <td>Rajendra Setia</td>\n",
       "      <td>TPG, Norwest Venture Partners, Evolvence India</td>\n",
       "      <td>$33,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company/Brand  Founded HeadQuarter                   Sector  \\\n",
       "0    Bombay Shaving      NaN         NaN                Ecommerce   \n",
       "1         Ruangguru   2014.0      Mumbai                   Edtech   \n",
       "2          Eduisfun      NaN      Mumbai                   Edtech   \n",
       "3          HomeLane   2014.0     Chennai          Interior design   \n",
       "4          Nu Genes   2004.0   Telangana                 AgriTech   \n",
       "..              ...      ...         ...                      ...   \n",
       "84     Infra.Market      NaN      Mumbai                Infratech   \n",
       "85              Oyo   2013.0    Gurugram              Hospitality   \n",
       "86       GoMechanic   2016.0       Delhi  Automobile & Technology   \n",
       "87           Spinny   2015.0       Delhi               Automobile   \n",
       "88  Ess Kay Fincorp      NaN   Rajasthan                  Banking   \n",
       "\n",
       "                                         What it does  \\\n",
       "0          Provides a range of male grooming products   \n",
       "1   A learning platform that provides topic-based ...   \n",
       "2             It aims to make learning fun via games.   \n",
       "3               Provides interior designing solutions   \n",
       "4   It is a seed company engaged in production, pr...   \n",
       "..                                                ...   \n",
       "84  It connects client requirements to their suppl...   \n",
       "85                Provides rooms for comfortable stay   \n",
       "86  Find automobile repair and maintenance service...   \n",
       "87                                Online car retailer   \n",
       "88              Organised Non-Banking Finance Company   \n",
       "\n",
       "                                             Founders  \\\n",
       "0                                  Shantanu Deshpande   \n",
       "1               Adamas Belva Syah Devara, Iman Usman.   \n",
       "2                                       Jatin Solanki   \n",
       "3                        Srikanth Iyer, Rama Harinath   \n",
       "4                              Narayana Reddy Punyala   \n",
       "..                                                ...   \n",
       "84                    Aaditya Sharda, Souvik Sengupta   \n",
       "85                                     Ritesh Agarwal   \n",
       "86  Amit Bhasin, Kushal Karwa, Nitin Rana, Rishabh...   \n",
       "87  Niraj Singh, Ramanshu Mahaur, Ganesh Pawar, Mo...   \n",
       "88                                     Rajendra Setia   \n",
       "\n",
       "                                             Investor     Amount($)  \\\n",
       "0                                Sixth Sense Ventures    $6,300,000   \n",
       "1                                    General Atlantic  $150,000,000   \n",
       "2      Deepak Parekh, Amitabh Bachchan, Piyush Pandey   $28,000,000   \n",
       "3   Evolvence India Fund (EIF), Pidilite Group, FJ...   $30,000,000   \n",
       "4            Innovation in Food and Agriculture (IFA)    $6,000,000   \n",
       "..                                                ...           ...   \n",
       "84  Tiger Global, Nexus Venture Partners, Accel Pa...   $20,000,000   \n",
       "85  MyPreferred Transformation, Avendus Finance, S...  $693,000,000   \n",
       "86                                    Sequoia Capital    $5,000,000   \n",
       "87  Norwest Venture Partners, General Catalyst, Fu...   $50,000,000   \n",
       "88     TPG, Norwest Venture Partners, Evolvence India   $33,000,000   \n",
       "\n",
       "            Stage  \n",
       "0             NaN  \n",
       "1        Series C  \n",
       "2   Fresh funding  \n",
       "3        Series D  \n",
       "4             NaN  \n",
       "..            ...  \n",
       "84       Series A  \n",
       "85            NaN  \n",
       "86       Series B  \n",
       "87            NaN  \n",
       "88            NaN  \n",
       "\n",
       "[89 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_19.info(), \"\\n\")\n",
    "data_19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66b1d55b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 89 entries, 0 to 88\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Company/Brand    89 non-null     object \n",
      " 1   Founded          60 non-null     object \n",
      " 2   HeadQuarter      70 non-null     object \n",
      " 3   Sector           84 non-null     object \n",
      " 4   What it does     89 non-null     object \n",
      " 5   Founders         86 non-null     object \n",
      " 6   Investor         89 non-null     object \n",
      " 7   Amount($)        77 non-null     float64\n",
      " 8   Stage            43 non-null     object \n",
      " 9   Year of Funding  89 non-null     object \n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 7.1+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Year of Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bombay Shaving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecommerce</td>\n",
       "      <td>Provides a range of male grooming products</td>\n",
       "      <td>Shantanu Deshpande</td>\n",
       "      <td>Sixth Sense Ventures</td>\n",
       "      <td>6300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ruangguru</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>A learning platform that provides topic-based ...</td>\n",
       "      <td>Adamas Belva Syah Devara, Iman Usman.</td>\n",
       "      <td>General Atlantic</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>Series C</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eduisfun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>It aims to make learning fun via games.</td>\n",
       "      <td>Jatin Solanki</td>\n",
       "      <td>Deepak Parekh, Amitabh Bachchan, Piyush Pandey</td>\n",
       "      <td>28000000.0</td>\n",
       "      <td>Fresh funding</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HomeLane</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Interior design</td>\n",
       "      <td>Provides interior designing solutions</td>\n",
       "      <td>Srikanth Iyer, Rama Harinath</td>\n",
       "      <td>Evolvence India Fund (EIF), Pidilite Group, FJ...</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nu Genes</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>It is a seed company engaged in production, pr...</td>\n",
       "      <td>Narayana Reddy Punyala</td>\n",
       "      <td>Innovation in Food and Agriculture (IFA)</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Infra.Market</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Infratech</td>\n",
       "      <td>It connects client requirements to their suppl...</td>\n",
       "      <td>Aaditya Sharda, Souvik Sengupta</td>\n",
       "      <td>Tiger Global, Nexus Venture Partners, Accel Pa...</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>Series A</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Oyo</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Hospitality</td>\n",
       "      <td>Provides rooms for comfortable stay</td>\n",
       "      <td>Ritesh Agarwal</td>\n",
       "      <td>MyPreferred Transformation, Avendus Finance, S...</td>\n",
       "      <td>693000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>GoMechanic</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Automobile &amp; Technology</td>\n",
       "      <td>Find automobile repair and maintenance service...</td>\n",
       "      <td>Amit Bhasin, Kushal Karwa, Nitin Rana, Rishabh...</td>\n",
       "      <td>Sequoia Capital</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>Series B</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Spinny</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Online car retailer</td>\n",
       "      <td>Niraj Singh, Ramanshu Mahaur, Ganesh Pawar, Mo...</td>\n",
       "      <td>Norwest Venture Partners, General Catalyst, Fu...</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Ess Kay Fincorp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Banking</td>\n",
       "      <td>Organised Non-Banking Finance Company</td>\n",
       "      <td>Rajendra Setia</td>\n",
       "      <td>TPG, Norwest Venture Partners, Evolvence India</td>\n",
       "      <td>33000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company/Brand Founded HeadQuarter                   Sector  \\\n",
       "0    Bombay Shaving     NaN         NaN                Ecommerce   \n",
       "1         Ruangguru  2014.0      Mumbai                   Edtech   \n",
       "2          Eduisfun     NaN      Mumbai                   Edtech   \n",
       "3          HomeLane  2014.0     Chennai          Interior design   \n",
       "4          Nu Genes  2004.0   Telangana                 AgriTech   \n",
       "..              ...     ...         ...                      ...   \n",
       "84     Infra.Market     NaN      Mumbai                Infratech   \n",
       "85              Oyo  2013.0    Gurugram              Hospitality   \n",
       "86       GoMechanic  2016.0       Delhi  Automobile & Technology   \n",
       "87           Spinny  2015.0       Delhi               Automobile   \n",
       "88  Ess Kay Fincorp     NaN   Rajasthan                  Banking   \n",
       "\n",
       "                                         What it does  \\\n",
       "0          Provides a range of male grooming products   \n",
       "1   A learning platform that provides topic-based ...   \n",
       "2             It aims to make learning fun via games.   \n",
       "3               Provides interior designing solutions   \n",
       "4   It is a seed company engaged in production, pr...   \n",
       "..                                                ...   \n",
       "84  It connects client requirements to their suppl...   \n",
       "85                Provides rooms for comfortable stay   \n",
       "86  Find automobile repair and maintenance service...   \n",
       "87                                Online car retailer   \n",
       "88              Organised Non-Banking Finance Company   \n",
       "\n",
       "                                             Founders  \\\n",
       "0                                  Shantanu Deshpande   \n",
       "1               Adamas Belva Syah Devara, Iman Usman.   \n",
       "2                                       Jatin Solanki   \n",
       "3                        Srikanth Iyer, Rama Harinath   \n",
       "4                              Narayana Reddy Punyala   \n",
       "..                                                ...   \n",
       "84                    Aaditya Sharda, Souvik Sengupta   \n",
       "85                                     Ritesh Agarwal   \n",
       "86  Amit Bhasin, Kushal Karwa, Nitin Rana, Rishabh...   \n",
       "87  Niraj Singh, Ramanshu Mahaur, Ganesh Pawar, Mo...   \n",
       "88                                     Rajendra Setia   \n",
       "\n",
       "                                             Investor    Amount($)  \\\n",
       "0                                Sixth Sense Ventures    6300000.0   \n",
       "1                                    General Atlantic  150000000.0   \n",
       "2      Deepak Parekh, Amitabh Bachchan, Piyush Pandey   28000000.0   \n",
       "3   Evolvence India Fund (EIF), Pidilite Group, FJ...   30000000.0   \n",
       "4            Innovation in Food and Agriculture (IFA)    6000000.0   \n",
       "..                                                ...          ...   \n",
       "84  Tiger Global, Nexus Venture Partners, Accel Pa...   20000000.0   \n",
       "85  MyPreferred Transformation, Avendus Finance, S...  693000000.0   \n",
       "86                                    Sequoia Capital    5000000.0   \n",
       "87  Norwest Venture Partners, General Catalyst, Fu...   50000000.0   \n",
       "88     TPG, Norwest Venture Partners, Evolvence India   33000000.0   \n",
       "\n",
       "            Stage Year of Funding  \n",
       "0             NaN            2019  \n",
       "1        Series C            2019  \n",
       "2   Fresh funding            2019  \n",
       "3        Series D            2019  \n",
       "4             NaN            2019  \n",
       "..            ...             ...  \n",
       "84       Series A            2019  \n",
       "85            NaN            2019  \n",
       "86       Series B            2019  \n",
       "87            NaN            2019  \n",
       "88            NaN            2019  \n",
       "\n",
       "[89 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the Founded column to a string\n",
    "data_19['Founded'] = data_19['Founded'].apply(str)\n",
    "\n",
    "# Removing the commas and dashes from the Amounts\n",
    "data_19[\"Amount($)\"] = data_19[\"Amount($)\"].apply(str)\n",
    "data_19[\"Amount($)\"].replace(\",\", \"\", inplace = True, regex=True)\n",
    "data_19[\"Amount($)\"].replace(\"Undisclosed\", np.nan, inplace = True, regex=True)\n",
    "data_19[\"Amount($)\"] = data_19[\"Amount($)\"].apply(lambda x: float(str(x).replace(\"$\",\"\")))\n",
    "data_19[\"Founded\"].replace(\"nan\", np.nan, inplace = True, regex=True)\n",
    "\n",
    "# Appending years of funding to the respective dataframes\n",
    "data_19[\"Year of Funding\"] = \"2019\"\n",
    "\n",
    "print(data_19.info(), \"\\n\")\n",
    "data_19"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0768ab3",
   "metadata": {},
   "source": [
    "#### 3.6.3 Processing the 2020 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "080252c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1055 entries, 0 to 1054\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Company/Brand  1055 non-null   object\n",
      " 1   Founded        843 non-null    object\n",
      " 2   HeadQuarter    961 non-null    object\n",
      " 3   Sector         1042 non-null   object\n",
      " 4   What it does   1055 non-null   object\n",
      " 5   Founders       1043 non-null   object\n",
      " 6   Investor       1017 non-null   object\n",
      " 7   Amount($)      1052 non-null   object\n",
      " 8   Stage          591 non-null    object\n",
      " 9   Unnamed: 9     2 non-null      object\n",
      "dtypes: object(10)\n",
      "memory usage: 82.5+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aqgromalin</td>\n",
       "      <td>2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>Cultivating Ideas for Profit</td>\n",
       "      <td>Prasanna Manogaran, Bharani C L</td>\n",
       "      <td>Angel investors</td>\n",
       "      <td>$200,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Krayonnz</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>An academy-guardian-scholar centric ecosystem ...</td>\n",
       "      <td>Saurabh Dixit, Gurudutt Upadhyay</td>\n",
       "      <td>GSF Accelerator</td>\n",
       "      <td>$100,000</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PadCare Labs</td>\n",
       "      <td>2018</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Hygiene management</td>\n",
       "      <td>Converting bio-hazardous waste to harmless waste</td>\n",
       "      <td>Ajinkya Dhariya</td>\n",
       "      <td>Venture Center</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCOME</td>\n",
       "      <td>2020</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Escrow</td>\n",
       "      <td>Escrow-as-a-service platform</td>\n",
       "      <td>Ritesh Tiwari</td>\n",
       "      <td>Venture Catalysts, PointOne Capital</td>\n",
       "      <td>$400,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gramophone</td>\n",
       "      <td>2016</td>\n",
       "      <td>Indore</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>Gramophone is an AgTech platform enabling acce...</td>\n",
       "      <td>Ashish Rajan Singh, Harshit Gupta, Nishant Mah...</td>\n",
       "      <td>Siana Capital Management, Info Edge</td>\n",
       "      <td>$340,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>Leverage Edu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>AI enabled marketplace that provides career gu...</td>\n",
       "      <td>Akshay Chaturvedi</td>\n",
       "      <td>DSG Consumer Partners, Blume Ventures</td>\n",
       "      <td>$1,500,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>EpiFi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>It offers customers with a single interface fo...</td>\n",
       "      <td>Sujith Narayanan, Sumit Gwalani</td>\n",
       "      <td>Sequoia India, Ribbit Capital</td>\n",
       "      <td>$13,200,000</td>\n",
       "      <td>Seed Round</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>Purplle</td>\n",
       "      <td>2012</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Cosmetics</td>\n",
       "      <td>Online makeup and beauty products retailer</td>\n",
       "      <td>Manish Taneja, Rahul Dash</td>\n",
       "      <td>Verlinvest</td>\n",
       "      <td>$8,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>Shuttl</td>\n",
       "      <td>2015</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Transport</td>\n",
       "      <td>App based bus aggregator serice</td>\n",
       "      <td>Amit Singh, Deepanshu Malviya</td>\n",
       "      <td>SIG Global India Fund LLP.</td>\n",
       "      <td>$8,043,000</td>\n",
       "      <td>Series C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>Pando</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Logitech</td>\n",
       "      <td>Networked logistics management software</td>\n",
       "      <td>Jayakrishnan, Abhijeet Manohar</td>\n",
       "      <td>Chiratae Ventures</td>\n",
       "      <td>$9,000,000</td>\n",
       "      <td>Series A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1055 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company/Brand Founded HeadQuarter              Sector  \\\n",
       "0       Aqgromalin    2019     Chennai            AgriTech   \n",
       "1         Krayonnz    2019   Bangalore              EdTech   \n",
       "2     PadCare Labs    2018        Pune  Hygiene management   \n",
       "3            NCOME    2020   New Delhi              Escrow   \n",
       "4       Gramophone    2016      Indore            AgriTech   \n",
       "...            ...     ...         ...                 ...   \n",
       "1050  Leverage Edu     NaN       Delhi              Edtech   \n",
       "1051         EpiFi     NaN         NaN             Fintech   \n",
       "1052       Purplle    2012      Mumbai           Cosmetics   \n",
       "1053        Shuttl    2015       Delhi           Transport   \n",
       "1054         Pando    2017     Chennai            Logitech   \n",
       "\n",
       "                                           What it does  \\\n",
       "0                          Cultivating Ideas for Profit   \n",
       "1     An academy-guardian-scholar centric ecosystem ...   \n",
       "2      Converting bio-hazardous waste to harmless waste   \n",
       "3                          Escrow-as-a-service platform   \n",
       "4     Gramophone is an AgTech platform enabling acce...   \n",
       "...                                                 ...   \n",
       "1050  AI enabled marketplace that provides career gu...   \n",
       "1051  It offers customers with a single interface fo...   \n",
       "1052         Online makeup and beauty products retailer   \n",
       "1053                    App based bus aggregator serice   \n",
       "1054            Networked logistics management software   \n",
       "\n",
       "                                               Founders  \\\n",
       "0                       Prasanna Manogaran, Bharani C L   \n",
       "1                      Saurabh Dixit, Gurudutt Upadhyay   \n",
       "2                                       Ajinkya Dhariya   \n",
       "3                                         Ritesh Tiwari   \n",
       "4     Ashish Rajan Singh, Harshit Gupta, Nishant Mah...   \n",
       "...                                                 ...   \n",
       "1050                                  Akshay Chaturvedi   \n",
       "1051                    Sujith Narayanan, Sumit Gwalani   \n",
       "1052                          Manish Taneja, Rahul Dash   \n",
       "1053                      Amit Singh, Deepanshu Malviya   \n",
       "1054                     Jayakrishnan, Abhijeet Manohar   \n",
       "\n",
       "                                   Investor    Amount($)       Stage  \\\n",
       "0                           Angel investors     $200,000         NaN   \n",
       "1                           GSF Accelerator     $100,000    Pre-seed   \n",
       "2                            Venture Center  Undisclosed    Pre-seed   \n",
       "3       Venture Catalysts, PointOne Capital     $400,000         NaN   \n",
       "4       Siana Capital Management, Info Edge     $340,000         NaN   \n",
       "...                                     ...          ...         ...   \n",
       "1050  DSG Consumer Partners, Blume Ventures   $1,500,000         NaN   \n",
       "1051          Sequoia India, Ribbit Capital  $13,200,000  Seed Round   \n",
       "1052                             Verlinvest   $8,000,000         NaN   \n",
       "1053             SIG Global India Fund LLP.   $8,043,000    Series C   \n",
       "1054                      Chiratae Ventures   $9,000,000    Series A   \n",
       "\n",
       "     Unnamed: 9  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "1050        NaN  \n",
       "1051        NaN  \n",
       "1052        NaN  \n",
       "1053        NaN  \n",
       "1054        NaN  \n",
       "\n",
       "[1055 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_20.info(), \"\\n\")\n",
    "data_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3a77ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the extra column in the 2020 DataFrame\n",
    "data_20 = data_20.iloc[: , :-1]\n",
    "data_20\n",
    "\n",
    "# Selecting the first value as Headquarters\n",
    "data_20['HeadQuarter'] = data_20['HeadQuarter'].apply(str)\n",
    "data_20['HeadQuarter'] = data_20['HeadQuarter'].str.split(',').str[0]\n",
    "data_20['HeadQuarter'] = data_20['HeadQuarter'].replace(\"'\", \"\", regex=True)\n",
    "\n",
    "# Removing the commas and dashes from the Amounts\n",
    "data_20[\"Amount($)\"] = data_20[\"Amount($)\"].apply(str)\n",
    "data_20[\"Amount($)\"].replace(\",\", \"\", inplace = True, regex=True)\n",
    "data_20[\"Amount($)\"].replace(\"Undisclosed\", np.nan, inplace = True, regex=True)\n",
    "data_20[\"Amount($)\"].replace(\"Undiclsosed\", np.nan, inplace = True, regex=True)\n",
    "data_20[\"Amount($)\"].replace(\"Undislosed\", np.nan, inplace = True, regex=True)\n",
    "data_20[\"Amount($)\"].replace(\"nan\", np.nan, inplace = True, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6a6eb65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From here, it is seen that True Balance has 2 entries in this DataFrame, both representing funding they received.\n",
    "# The entry with index 465 will be dropped and the Stage for 136 corrected\n",
    "data_20.loc[data_20[\"Company/Brand\"] == \"True Balance\", \"Stage\"] = \"Series C\"\n",
    "data_20.drop([465], axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb77ddfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Eruditus</td>\n",
       "      <td>2010</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Education</td>\n",
       "      <td>Bring world-class business and professional ed...</td>\n",
       "      <td>Chaitanya Kalipatnapu, Ashwin Damera</td>\n",
       "      <td>Bertelsmann India Investments, Sequoia Capital...</td>\n",
       "      <td>113000000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company/Brand Founded HeadQuarter     Sector  \\\n",
       "472      Eruditus    2010      Mumbai  Education   \n",
       "\n",
       "                                          What it does  \\\n",
       "472  Bring world-class business and professional ed...   \n",
       "\n",
       "                                 Founders  \\\n",
       "472  Chaitanya Kalipatnapu, Ashwin Damera   \n",
       "\n",
       "                                              Investor  Amount($)     Stage  \n",
       "472  Bertelsmann India Investments, Sequoia Capital...  113000000  Series D  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eruditus also has an erroenously stated amount, which is to be corrected\n",
    "data_20.loc[data_20[\"Company/Brand\"] == \"Eruditus\", [\"Amount($)\", \"Stage\"]] = [113000000, \"Series D\"]\n",
    "data_20.loc[data_20[\"Company/Brand\"] == \"Eruditus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a731e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1054 entries, 0 to 1054\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Company/Brand    1054 non-null   object \n",
      " 1   Founded          842 non-null    object \n",
      " 2   HeadQuarter      1054 non-null   object \n",
      " 3   Sector           1041 non-null   object \n",
      " 4   What it does     1054 non-null   object \n",
      " 5   Founders         1042 non-null   object \n",
      " 6   Investor         1016 non-null   object \n",
      " 7   Amount($)        806 non-null    float64\n",
      " 8   Stage            592 non-null    object \n",
      " 9   Year of Funding  1054 non-null   object \n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 90.6+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Year of Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aqgromalin</td>\n",
       "      <td>2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>Cultivating Ideas for Profit</td>\n",
       "      <td>Prasanna Manogaran, Bharani C L</td>\n",
       "      <td>Angel investors</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Krayonnz</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>An academy-guardian-scholar centric ecosystem ...</td>\n",
       "      <td>Saurabh Dixit, Gurudutt Upadhyay</td>\n",
       "      <td>GSF Accelerator</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PadCare Labs</td>\n",
       "      <td>2018</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Hygiene management</td>\n",
       "      <td>Converting bio-hazardous waste to harmless waste</td>\n",
       "      <td>Ajinkya Dhariya</td>\n",
       "      <td>Venture Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCOME</td>\n",
       "      <td>2020</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Escrow</td>\n",
       "      <td>Escrow-as-a-service platform</td>\n",
       "      <td>Ritesh Tiwari</td>\n",
       "      <td>Venture Catalysts, PointOne Capital</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gramophone</td>\n",
       "      <td>2016</td>\n",
       "      <td>Indore</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>Gramophone is an AgTech platform enabling acce...</td>\n",
       "      <td>Ashish Rajan Singh, Harshit Gupta, Nishant Mah...</td>\n",
       "      <td>Siana Capital Management, Info Edge</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>Leverage Edu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>AI enabled marketplace that provides career gu...</td>\n",
       "      <td>Akshay Chaturvedi</td>\n",
       "      <td>DSG Consumer Partners, Blume Ventures</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>EpiFi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>It offers customers with a single interface fo...</td>\n",
       "      <td>Sujith Narayanan, Sumit Gwalani</td>\n",
       "      <td>Sequoia India, Ribbit Capital</td>\n",
       "      <td>13200000.0</td>\n",
       "      <td>Seed Round</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>Purplle</td>\n",
       "      <td>2012</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Cosmetics</td>\n",
       "      <td>Online makeup and beauty products retailer</td>\n",
       "      <td>Manish Taneja, Rahul Dash</td>\n",
       "      <td>Verlinvest</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>Shuttl</td>\n",
       "      <td>2015</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Transport</td>\n",
       "      <td>App based bus aggregator serice</td>\n",
       "      <td>Amit Singh, Deepanshu Malviya</td>\n",
       "      <td>SIG Global India Fund LLP.</td>\n",
       "      <td>8043000.0</td>\n",
       "      <td>Series C</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>Pando</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Logitech</td>\n",
       "      <td>Networked logistics management software</td>\n",
       "      <td>Jayakrishnan, Abhijeet Manohar</td>\n",
       "      <td>Chiratae Ventures</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>Series A</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1054 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company/Brand Founded HeadQuarter              Sector  \\\n",
       "0       Aqgromalin    2019     Chennai            AgriTech   \n",
       "1         Krayonnz    2019   Bangalore              EdTech   \n",
       "2     PadCare Labs    2018        Pune  Hygiene management   \n",
       "3            NCOME    2020   New Delhi              Escrow   \n",
       "4       Gramophone    2016      Indore            AgriTech   \n",
       "...            ...     ...         ...                 ...   \n",
       "1050  Leverage Edu     NaN       Delhi              Edtech   \n",
       "1051         EpiFi     NaN         nan             Fintech   \n",
       "1052       Purplle    2012      Mumbai           Cosmetics   \n",
       "1053        Shuttl    2015       Delhi           Transport   \n",
       "1054         Pando    2017     Chennai            Logitech   \n",
       "\n",
       "                                           What it does  \\\n",
       "0                          Cultivating Ideas for Profit   \n",
       "1     An academy-guardian-scholar centric ecosystem ...   \n",
       "2      Converting bio-hazardous waste to harmless waste   \n",
       "3                          Escrow-as-a-service platform   \n",
       "4     Gramophone is an AgTech platform enabling acce...   \n",
       "...                                                 ...   \n",
       "1050  AI enabled marketplace that provides career gu...   \n",
       "1051  It offers customers with a single interface fo...   \n",
       "1052         Online makeup and beauty products retailer   \n",
       "1053                    App based bus aggregator serice   \n",
       "1054            Networked logistics management software   \n",
       "\n",
       "                                               Founders  \\\n",
       "0                       Prasanna Manogaran, Bharani C L   \n",
       "1                      Saurabh Dixit, Gurudutt Upadhyay   \n",
       "2                                       Ajinkya Dhariya   \n",
       "3                                         Ritesh Tiwari   \n",
       "4     Ashish Rajan Singh, Harshit Gupta, Nishant Mah...   \n",
       "...                                                 ...   \n",
       "1050                                  Akshay Chaturvedi   \n",
       "1051                    Sujith Narayanan, Sumit Gwalani   \n",
       "1052                          Manish Taneja, Rahul Dash   \n",
       "1053                      Amit Singh, Deepanshu Malviya   \n",
       "1054                     Jayakrishnan, Abhijeet Manohar   \n",
       "\n",
       "                                   Investor   Amount($)       Stage  \\\n",
       "0                           Angel investors    200000.0         NaN   \n",
       "1                           GSF Accelerator    100000.0    Pre-seed   \n",
       "2                            Venture Center         NaN    Pre-seed   \n",
       "3       Venture Catalysts, PointOne Capital    400000.0         NaN   \n",
       "4       Siana Capital Management, Info Edge    340000.0         NaN   \n",
       "...                                     ...         ...         ...   \n",
       "1050  DSG Consumer Partners, Blume Ventures   1500000.0         NaN   \n",
       "1051          Sequoia India, Ribbit Capital  13200000.0  Seed Round   \n",
       "1052                             Verlinvest   8000000.0         NaN   \n",
       "1053             SIG Global India Fund LLP.   8043000.0    Series C   \n",
       "1054                      Chiratae Ventures   9000000.0    Series A   \n",
       "\n",
       "     Year of Funding  \n",
       "0               2020  \n",
       "1               2020  \n",
       "2               2020  \n",
       "3               2020  \n",
       "4               2020  \n",
       "...              ...  \n",
       "1050            2020  \n",
       "1051            2020  \n",
       "1052            2020  \n",
       "1053            2020  \n",
       "1054            2020  \n",
       "\n",
       "[1054 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing the $ signs and converting the Amount column to float\n",
    "data_20[\"Amount($)\"] = data_20[\"Amount($)\"].apply(lambda x: float(str(x).replace(\"$\",\"\")))\n",
    "\n",
    "# Appending years of funding to the respective dataframes\n",
    "data_20[\"Year of Funding\"] = \"2020\"\n",
    "\n",
    "print(data_20.info(), \"\\n\")\n",
    "data_20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c285a0",
   "metadata": {},
   "source": [
    "#### 3.6.4 Processing the 2021 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c009bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1209 entries, 0 to 1208\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company/Brand  1209 non-null   object \n",
      " 1   Founded        1208 non-null   float64\n",
      " 2   HeadQuarter    1208 non-null   object \n",
      " 3   Sector         1209 non-null   object \n",
      " 4   What it does   1209 non-null   object \n",
      " 5   Founders       1205 non-null   object \n",
      " 6   Investor       1147 non-null   object \n",
      " 7   Amount($)      1206 non-null   object \n",
      " 8   Stage          781 non-null    object \n",
      "dtypes: float64(1), object(8)\n",
      "memory usage: 85.1+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unbox Robotics</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>AI startup</td>\n",
       "      <td>Unbox Robotics builds on-demand AI-driven ware...</td>\n",
       "      <td>Pramod Ghadge, Shahid Memon</td>\n",
       "      <td>BEENEXT, Entrepreneur First</td>\n",
       "      <td>$1,200,000</td>\n",
       "      <td>Pre-series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>upGrad</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>UpGrad is an online higher education platform.</td>\n",
       "      <td>Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...</td>\n",
       "      <td>Unilazer Ventures, IIFL Asset Management</td>\n",
       "      <td>$120,000,000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lead School</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>LEAD School offers technology based school tra...</td>\n",
       "      <td>Smita Deorah, Sumeet Mehta</td>\n",
       "      <td>GSV Ventures, Westbridge Capital</td>\n",
       "      <td>$30,000,000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bizongo</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>B2B E-commerce</td>\n",
       "      <td>Bizongo is a business-to-business online marke...</td>\n",
       "      <td>Aniket Deb, Ankit Tomar, Sachin Agrawal</td>\n",
       "      <td>CDC Group, IDG Capital</td>\n",
       "      <td>$51,000,000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FypMoney</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>FypMoney is Digital NEO Bank for Teenagers, em...</td>\n",
       "      <td>Kapil Banwari</td>\n",
       "      <td>Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal</td>\n",
       "      <td>$2,000,000</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>Gigforce</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Staffing &amp; Recruiting</td>\n",
       "      <td>A gig/on-demand staffing company.</td>\n",
       "      <td>Chirag Mittal, Anirudh Syal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "      <td>$3000000</td>\n",
       "      <td>Pre-series A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>Vahdam</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Food &amp; Beverages</td>\n",
       "      <td>VAHDAM is among the world’s first vertically i...</td>\n",
       "      <td>Bala Sarda</td>\n",
       "      <td>IIFL AMC</td>\n",
       "      <td>$20000000</td>\n",
       "      <td>Series D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>Leap Finance</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>International education loans for high potenti...</td>\n",
       "      <td>Arnav Kumar, Vaibhav Singh</td>\n",
       "      <td>Owl Ventures</td>\n",
       "      <td>$55000000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>CollegeDekho</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Collegedekho.com is Student’s Partner, Friend ...</td>\n",
       "      <td>Ruchir Arora</td>\n",
       "      <td>Winter Capital, ETS, Man Capital</td>\n",
       "      <td>$26000000</td>\n",
       "      <td>Series B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>WeRize</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>Vishal Chopra, Himanshu Gupta</td>\n",
       "      <td>3one4 Capital, Kalaari Capital</td>\n",
       "      <td>$8000000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1209 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company/Brand  Founded HeadQuarter                 Sector  \\\n",
       "0     Unbox Robotics   2019.0   Bangalore             AI startup   \n",
       "1             upGrad   2015.0      Mumbai                 EdTech   \n",
       "2        Lead School   2012.0      Mumbai                 EdTech   \n",
       "3            Bizongo   2015.0      Mumbai         B2B E-commerce   \n",
       "4           FypMoney   2021.0    Gurugram                FinTech   \n",
       "...              ...      ...         ...                    ...   \n",
       "1204        Gigforce   2019.0    Gurugram  Staffing & Recruiting   \n",
       "1205          Vahdam   2015.0   New Delhi       Food & Beverages   \n",
       "1206    Leap Finance   2019.0   Bangalore     Financial Services   \n",
       "1207    CollegeDekho   2015.0    Gurugram                 EdTech   \n",
       "1208          WeRize   2019.0   Bangalore     Financial Services   \n",
       "\n",
       "                                           What it does  \\\n",
       "0     Unbox Robotics builds on-demand AI-driven ware...   \n",
       "1        UpGrad is an online higher education platform.   \n",
       "2     LEAD School offers technology based school tra...   \n",
       "3     Bizongo is a business-to-business online marke...   \n",
       "4     FypMoney is Digital NEO Bank for Teenagers, em...   \n",
       "...                                                 ...   \n",
       "1204                  A gig/on-demand staffing company.   \n",
       "1205  VAHDAM is among the world’s first vertically i...   \n",
       "1206  International education loans for high potenti...   \n",
       "1207  Collegedekho.com is Student’s Partner, Friend ...   \n",
       "1208  India’s first socially distributed full stack ...   \n",
       "\n",
       "                                               Founders  \\\n",
       "0                           Pramod Ghadge, Shahid Memon   \n",
       "1     Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...   \n",
       "2                            Smita Deorah, Sumeet Mehta   \n",
       "3               Aniket Deb, Ankit Tomar, Sachin Agrawal   \n",
       "4                                         Kapil Banwari   \n",
       "...                                                 ...   \n",
       "1204                        Chirag Mittal, Anirudh Syal   \n",
       "1205                                         Bala Sarda   \n",
       "1206                         Arnav Kumar, Vaibhav Singh   \n",
       "1207                                       Ruchir Arora   \n",
       "1208                      Vishal Chopra, Himanshu Gupta   \n",
       "\n",
       "                                           Investor     Amount($)  \\\n",
       "0                       BEENEXT, Entrepreneur First    $1,200,000   \n",
       "1          Unilazer Ventures, IIFL Asset Management  $120,000,000   \n",
       "2                  GSV Ventures, Westbridge Capital   $30,000,000   \n",
       "3                            CDC Group, IDG Capital   $51,000,000   \n",
       "4     Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal    $2,000,000   \n",
       "...                                             ...           ...   \n",
       "1204                                Endiya Partners      $3000000   \n",
       "1205                                       IIFL AMC     $20000000   \n",
       "1206                                   Owl Ventures     $55000000   \n",
       "1207               Winter Capital, ETS, Man Capital     $26000000   \n",
       "1208                 3one4 Capital, Kalaari Capital      $8000000   \n",
       "\n",
       "             Stage  \n",
       "0     Pre-series A  \n",
       "1              NaN  \n",
       "2         Series D  \n",
       "3         Series C  \n",
       "4             Seed  \n",
       "...            ...  \n",
       "1204  Pre-series A  \n",
       "1205      Series D  \n",
       "1206      Series C  \n",
       "1207      Series B  \n",
       "1208      Series A  \n",
       "\n",
       "[1209 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data_21.info(), \"\\n\")\n",
    "data_21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45ac8d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the Founded column to a string\n",
    "data_21['Founded'] = data_21['Founded'].apply(str)\n",
    "data_21[\"Founded\"].replace(\"nan\", np.nan, inplace = True, regex=True)\n",
    "\n",
    "# Removing the commas and dashes from the Amounts\n",
    "data_21[\"Amount($)\"] = data_21[\"Amount($)\"].apply(str)\n",
    "data_21[\"Amount($)\"].replace(\",\", \"\", inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"].replace(\"Undisclosed\", np.nan, inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"].replace(\"Undiclsosed\", np.nan, inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"].replace(\"Undislosed\", np.nan, inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"].replace(\"undisclosed\", np.nan, inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"].replace(\"nan\", np.nan, inplace = True, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "543d1f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>FanPlay</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Computer Games</td>\n",
       "      <td>Computer Games</td>\n",
       "      <td>A real money game app specializing in trivia g...</td>\n",
       "      <td>YC W21</td>\n",
       "      <td>Upsparks</td>\n",
       "      <td>1200000</td>\n",
       "      <td>Series A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company/Brand Founded     HeadQuarter          Sector  \\\n",
       "111       FanPlay  2020.0  Computer Games  Computer Games   \n",
       "\n",
       "                                          What it does Founders  Investor  \\\n",
       "111  A real money game app specializing in trivia g...   YC W21  Upsparks   \n",
       "\n",
       "    Amount($)     Stage  \n",
       "111   1200000  Series A  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FanPlay has duplicates with indexes 1768 and 1781, and has wrongly placed amounts.\n",
    "data_21.drop([98], axis = 0, inplace = True)\n",
    "\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"FanPlay\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [1200000, \"Series A\", \"Upsparks\"]\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"FanPlay\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ccbb9304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Fullife Healthcare</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Pharmaceuticals\\t#REF!</td>\n",
       "      <td>Primary Business is Development and Manufactur...</td>\n",
       "      <td>Varun Khanna</td>\n",
       "      <td>Morgan Stanley Private Equity Asia</td>\n",
       "      <td>Morgan Stanley Private Equity Asia</td>\n",
       "      <td>22000000</td>\n",
       "      <td>Series C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company/Brand Founded             HeadQuarter  \\\n",
       "242  Fullife Healthcare  2009.0  Pharmaceuticals\\t#REF!   \n",
       "\n",
       "                                                Sector  What it does  \\\n",
       "242  Primary Business is Development and Manufactur...  Varun Khanna   \n",
       "\n",
       "                               Founders                            Investor  \\\n",
       "242  Morgan Stanley Private Equity Asia  Morgan Stanley Private Equity Asia   \n",
       "\n",
       "    Amount($)     Stage  \n",
       "242  22000000  Series C  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Fullife Healthcare, the entries with indexes 1912 and 1926 both represent Series C funding raised in 2021. One has to be deleted.\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"Fullife Healthcare\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [22000000, \"Series C\", \"Morgan Stanley Private Equity Asia\"]\n",
    "data_21.drop([256], axis = 0, inplace = True)\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"Fullife Healthcare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "353cc17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yet another error is thrown when trying to convert Amount to a float, this time by an entry \"Seed\", which has to be checked and corrected\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"Seed\"]\n",
    "\n",
    "# Correcting for MoEVing\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"MoEVing\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [5000000, \"Seed\", np.nan]\n",
    "\n",
    "# Correcting for Godamwale\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"Godamwale\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [1000000, \"Seed\", \"Anand Aryamane\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f110b976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Little Leap</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Soft Skills that make Smart Leaders</td>\n",
       "      <td>Holistic Development Programs for children in ...</td>\n",
       "      <td>ah! Ventures</td>\n",
       "      <td>361133</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company/Brand Founded HeadQuarter  Sector  \\\n",
       "538   Little Leap  2020.0   New Delhi  EdTech   \n",
       "\n",
       "                            What it does  \\\n",
       "538  Soft Skills that make Smart Leaders   \n",
       "\n",
       "                                              Founders      Investor  \\\n",
       "538  Holistic Development Programs for children in ...  ah! Ventures   \n",
       "\n",
       "    Amount($) Stage  \n",
       "538    361133  Seed  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A similar error is thrown again, by an entry \"ah! Ventures\", which has to be checked and corrected\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"ah! Ventures\"]\n",
    "\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"Little Leap\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [int(26700000/73.9339), \"Seed\", \"ah! Ventures\"]\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"Little Leap\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "695e504c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>AdmitKard</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Noida</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>A tech solution for end to end career advisory...</td>\n",
       "      <td>Vamsi Krishna, Pulkit Jain, Gaurav Munjal\\t#REF!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361133</td>\n",
       "      <td>Pre-series A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company/Brand Founded HeadQuarter  Sector  \\\n",
       "545     AdmitKard  2016.0       Noida  EdTech   \n",
       "\n",
       "                                          What it does  \\\n",
       "545  A tech solution for end to end career advisory...   \n",
       "\n",
       "                                             Founders Investor Amount($)  \\\n",
       "545  Vamsi Krishna, Pulkit Jain, Gaurav Munjal\\t#REF!      NaN    361133   \n",
       "\n",
       "            Stage  \n",
       "545  Pre-series A  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A similar error is thrown again, by an entry \"Pre-series A\", which has to be checked and corrected\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"Pre-series A\"]\n",
    "\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"AdmitKard\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [int(26700000/73.9339), \"Pre-series A\", np.nan]\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"AdmitKard\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f679600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>BHyve</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>A Future of Work Platform for diffusing Employ...</td>\n",
       "      <td>Backed by 100x.VC</td>\n",
       "      <td>ITO Angel Network LetsVenture</td>\n",
       "      <td>300000</td>\n",
       "      <td>Angel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company/Brand Founded HeadQuarter           Sector  \\\n",
       "551         BHyve  2020.0      Mumbai  Human Resources   \n",
       "\n",
       "                                          What it does           Founders  \\\n",
       "551  A Future of Work Platform for diffusing Employ...  Backed by 100x.VC   \n",
       "\n",
       "                          Investor Amount($)  Stage  \n",
       "551  ITO Angel Network LetsVenture    300000  Angel  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A similar error is thrown again, by an entry \"ITO Angel Network LetsVenture\", which has to be checked and corrected\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"ITO Angel Network LetsVenture\"]\n",
    "\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"ITO Angel Network LetsVenture\", [\"Amount($)\", \"Stage\", \"Investor\"]] = [300000, \"Angel\", \"ITO Angel Network LetsVenture\"]\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"BHyve\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e0cc7215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>Saarthi Pedagogy</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Ahmadabad</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>India's fastest growing Pedagogy company, serv...</td>\n",
       "      <td>Pedagogy</td>\n",
       "      <td>Sushil Agarwal</td>\n",
       "      <td>1000000</td>\n",
       "      <td>Seed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Company/Brand Founded HeadQuarter  Sector  \\\n",
       "677  Saarthi Pedagogy  2015.0   Ahmadabad  EdTech   \n",
       "\n",
       "                                          What it does  Founders  \\\n",
       "677  India's fastest growing Pedagogy company, serv...  Pedagogy   \n",
       "\n",
       "           Investor Amount($) Stage  \n",
       "677  Sushil Agarwal   1000000  Seed  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A similar error is thrown again, by an entry \"ITO Angel Network LetsVenture\", which has to be checked and corrected\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"JITO Angel Network LetsVenture\"]\n",
    "\n",
    "data_21.loc[data_21[\"Amount($)\"] == \"JITO Angel Network LetsVenture\", [\"Amount($)\", \"Stage\"]] = [1000000, \"Seed\"]\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"Saarthi Pedagogy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "98c889ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>MYRE Capital</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Commercial Real Estate</td>\n",
       "      <td>Democratising Real Estate Ownership</td>\n",
       "      <td>Own rent yielding commercial properties</td>\n",
       "      <td>Aryaman Vir</td>\n",
       "      <td>9627286</td>\n",
       "      <td>Venture</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company/Brand Founded HeadQuarter                  Sector  \\\n",
       "674  MYRE Capital  2020.0      Mumbai  Commercial Real Estate   \n",
       "\n",
       "                            What it does  \\\n",
       "674  Democratising Real Estate Ownership   \n",
       "\n",
       "                                    Founders     Investor Amount($)    Stage  \n",
       "674  Own rent yielding commercial properties  Aryaman Vir   9627286  Venture  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_21.loc[data_21[\"Stage\"] == \"$6000000\"]\n",
    "\n",
    "data_21.loc[data_21[\"Stage\"] == \"$6000000\", [\"Amount($)\", \"Stage\"]] = [9627286, \"Venture\"]\n",
    "data_21.loc[data_21[\"Company/Brand\"] == \"MYRE Capital\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bece14aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1207 entries, 0 to 1208\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Company/Brand    1207 non-null   object \n",
      " 1   Founded          1206 non-null   object \n",
      " 2   HeadQuarter      1206 non-null   object \n",
      " 3   Sector           1207 non-null   object \n",
      " 4   What it does     1207 non-null   object \n",
      " 5   Founders         1203 non-null   object \n",
      " 6   Investor         1143 non-null   object \n",
      " 7   Amount($)        1205 non-null   float64\n",
      " 8   Stage            784 non-null    object \n",
      " 9   Year of Funding  1207 non-null   object \n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 103.7+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company/Brand</th>\n",
       "      <th>Founded</th>\n",
       "      <th>HeadQuarter</th>\n",
       "      <th>Sector</th>\n",
       "      <th>What it does</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investor</th>\n",
       "      <th>Amount($)</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Year of Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unbox Robotics</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>AI startup</td>\n",
       "      <td>Unbox Robotics builds on-demand AI-driven ware...</td>\n",
       "      <td>Pramod Ghadge, Shahid Memon</td>\n",
       "      <td>BEENEXT, Entrepreneur First</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>upGrad</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>UpGrad is an online higher education platform.</td>\n",
       "      <td>Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...</td>\n",
       "      <td>Unilazer Ventures, IIFL Asset Management</td>\n",
       "      <td>120000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lead School</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>LEAD School offers technology based school tra...</td>\n",
       "      <td>Smita Deorah, Sumeet Mehta</td>\n",
       "      <td>GSV Ventures, Westbridge Capital</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bizongo</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>B2B E-commerce</td>\n",
       "      <td>Bizongo is a business-to-business online marke...</td>\n",
       "      <td>Aniket Deb, Ankit Tomar, Sachin Agrawal</td>\n",
       "      <td>CDC Group, IDG Capital</td>\n",
       "      <td>51000000.0</td>\n",
       "      <td>Series C</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FypMoney</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>FypMoney is Digital NEO Bank for Teenagers, em...</td>\n",
       "      <td>Kapil Banwari</td>\n",
       "      <td>Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>Seed</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>Gigforce</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Staffing &amp; Recruiting</td>\n",
       "      <td>A gig/on-demand staffing company.</td>\n",
       "      <td>Chirag Mittal, Anirudh Syal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>Vahdam</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Food &amp; Beverages</td>\n",
       "      <td>VAHDAM is among the world’s first vertically i...</td>\n",
       "      <td>Bala Sarda</td>\n",
       "      <td>IIFL AMC</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>Leap Finance</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>International education loans for high potenti...</td>\n",
       "      <td>Arnav Kumar, Vaibhav Singh</td>\n",
       "      <td>Owl Ventures</td>\n",
       "      <td>55000000.0</td>\n",
       "      <td>Series C</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>CollegeDekho</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Collegedekho.com is Student’s Partner, Friend ...</td>\n",
       "      <td>Ruchir Arora</td>\n",
       "      <td>Winter Capital, ETS, Man Capital</td>\n",
       "      <td>26000000.0</td>\n",
       "      <td>Series B</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>WeRize</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>Vishal Chopra, Himanshu Gupta</td>\n",
       "      <td>3one4 Capital, Kalaari Capital</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>Series A</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1207 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company/Brand Founded HeadQuarter                 Sector  \\\n",
       "0     Unbox Robotics  2019.0   Bangalore             AI startup   \n",
       "1             upGrad  2015.0      Mumbai                 EdTech   \n",
       "2        Lead School  2012.0      Mumbai                 EdTech   \n",
       "3            Bizongo  2015.0      Mumbai         B2B E-commerce   \n",
       "4           FypMoney  2021.0    Gurugram                FinTech   \n",
       "...              ...     ...         ...                    ...   \n",
       "1204        Gigforce  2019.0    Gurugram  Staffing & Recruiting   \n",
       "1205          Vahdam  2015.0   New Delhi       Food & Beverages   \n",
       "1206    Leap Finance  2019.0   Bangalore     Financial Services   \n",
       "1207    CollegeDekho  2015.0    Gurugram                 EdTech   \n",
       "1208          WeRize  2019.0   Bangalore     Financial Services   \n",
       "\n",
       "                                           What it does  \\\n",
       "0     Unbox Robotics builds on-demand AI-driven ware...   \n",
       "1        UpGrad is an online higher education platform.   \n",
       "2     LEAD School offers technology based school tra...   \n",
       "3     Bizongo is a business-to-business online marke...   \n",
       "4     FypMoney is Digital NEO Bank for Teenagers, em...   \n",
       "...                                                 ...   \n",
       "1204                  A gig/on-demand staffing company.   \n",
       "1205  VAHDAM is among the world’s first vertically i...   \n",
       "1206  International education loans for high potenti...   \n",
       "1207  Collegedekho.com is Student’s Partner, Friend ...   \n",
       "1208  India’s first socially distributed full stack ...   \n",
       "\n",
       "                                               Founders  \\\n",
       "0                           Pramod Ghadge, Shahid Memon   \n",
       "1     Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...   \n",
       "2                            Smita Deorah, Sumeet Mehta   \n",
       "3               Aniket Deb, Ankit Tomar, Sachin Agrawal   \n",
       "4                                         Kapil Banwari   \n",
       "...                                                 ...   \n",
       "1204                        Chirag Mittal, Anirudh Syal   \n",
       "1205                                         Bala Sarda   \n",
       "1206                         Arnav Kumar, Vaibhav Singh   \n",
       "1207                                       Ruchir Arora   \n",
       "1208                      Vishal Chopra, Himanshu Gupta   \n",
       "\n",
       "                                           Investor    Amount($)  \\\n",
       "0                       BEENEXT, Entrepreneur First    1200000.0   \n",
       "1          Unilazer Ventures, IIFL Asset Management  120000000.0   \n",
       "2                  GSV Ventures, Westbridge Capital   30000000.0   \n",
       "3                            CDC Group, IDG Capital   51000000.0   \n",
       "4     Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal    2000000.0   \n",
       "...                                             ...          ...   \n",
       "1204                                Endiya Partners    3000000.0   \n",
       "1205                                       IIFL AMC   20000000.0   \n",
       "1206                                   Owl Ventures   55000000.0   \n",
       "1207               Winter Capital, ETS, Man Capital   26000000.0   \n",
       "1208                 3one4 Capital, Kalaari Capital    8000000.0   \n",
       "\n",
       "             Stage Year of Funding  \n",
       "0     Pre-series A            2021  \n",
       "1              NaN            2021  \n",
       "2         Series D            2021  \n",
       "3         Series C            2021  \n",
       "4             Seed            2021  \n",
       "...            ...             ...  \n",
       "1204  Pre-series A            2021  \n",
       "1205      Series D            2021  \n",
       "1206      Series C            2021  \n",
       "1207      Series B            2021  \n",
       "1208      Series A            2021  \n",
       "\n",
       "[1207 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_21[\"Amount($)\"].replace('', 0, inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"] = data_21[\"Amount($)\"].apply(lambda x: str(x).replace(\"$\",\"\"))\n",
    "data_21[\"Amount($)\"].replace('nan', 0, inplace = True, regex=True)\n",
    "data_21[\"Amount($)\"] = pd.to_numeric(data_21[\"Amount($)\"])\n",
    "data_21[\"Amount($)\"] = data_21[\"Amount($)\"].apply(float)\n",
    "\n",
    "# Appending years of funding to the respective dataframes\n",
    "data_21[\"Year of Funding\"] = \"2021\"\n",
    "\n",
    "print(data_21.info(), \"\\n\")\n",
    "data_21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26388a95",
   "metadata": {},
   "source": [
    "### 3.7 Joining all the DataFrames into a combined DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca09aae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Description</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Funding Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bombay Shaving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecommerce</td>\n",
       "      <td>Provides a range of male grooming products</td>\n",
       "      <td>Shantanu Deshpande</td>\n",
       "      <td>Sixth Sense Ventures</td>\n",
       "      <td>6300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ruangguru</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>A learning platform that provides topic-based ...</td>\n",
       "      <td>Adamas Belva Syah Devara, Iman Usman.</td>\n",
       "      <td>General Atlantic</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>Series C</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eduisfun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>It aims to make learning fun via games.</td>\n",
       "      <td>Jatin Solanki</td>\n",
       "      <td>Deepak Parekh, Amitabh Bachchan, Piyush Pandey</td>\n",
       "      <td>28000000.0</td>\n",
       "      <td>Fresh funding</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HomeLane</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Interior design</td>\n",
       "      <td>Provides interior designing solutions</td>\n",
       "      <td>Srikanth Iyer, Rama Harinath</td>\n",
       "      <td>Evolvence India Fund (EIF), Pidilite Group, FJ...</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nu Genes</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>AgriTech</td>\n",
       "      <td>It is a seed company engaged in production, pr...</td>\n",
       "      <td>Narayana Reddy Punyala</td>\n",
       "      <td>Innovation in Food and Agriculture (IFA)</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>Gigforce</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Staffing &amp; Recruiting</td>\n",
       "      <td>A gig/on-demand staffing company.</td>\n",
       "      <td>Chirag Mittal, Anirudh Syal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>Vahdam</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Food &amp; Beverages</td>\n",
       "      <td>VAHDAM is among the world’s first vertically i...</td>\n",
       "      <td>Bala Sarda</td>\n",
       "      <td>IIFL AMC</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>Leap Finance</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>International education loans for high potenti...</td>\n",
       "      <td>Arnav Kumar, Vaibhav Singh</td>\n",
       "      <td>Owl Ventures</td>\n",
       "      <td>55000000.0</td>\n",
       "      <td>Series C</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>CollegeDekho</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Collegedekho.com is Student’s Partner, Friend ...</td>\n",
       "      <td>Ruchir Arora</td>\n",
       "      <td>Winter Capital, ETS, Man Capital</td>\n",
       "      <td>26000000.0</td>\n",
       "      <td>Series B</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>WeRize</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>Vishal Chopra, Himanshu Gupta</td>\n",
       "      <td>3one4 Capital, Kalaari Capital</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>Series A</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2350 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Company Name Year Founded Headquarters                 Sector  \\\n",
       "0     Bombay Shaving          NaN          NaN              Ecommerce   \n",
       "1          Ruangguru       2014.0       Mumbai                 Edtech   \n",
       "2           Eduisfun          NaN       Mumbai                 Edtech   \n",
       "3           HomeLane       2014.0      Chennai        Interior design   \n",
       "4           Nu Genes       2004.0    Telangana               AgriTech   \n",
       "...              ...          ...          ...                    ...   \n",
       "2345        Gigforce       2019.0     Gurugram  Staffing & Recruiting   \n",
       "2346          Vahdam       2015.0    New Delhi       Food & Beverages   \n",
       "2347    Leap Finance       2019.0    Bangalore     Financial Services   \n",
       "2348    CollegeDekho       2015.0     Gurugram                 EdTech   \n",
       "2349          WeRize       2019.0    Bangalore     Financial Services   \n",
       "\n",
       "                                            Description  \\\n",
       "0            Provides a range of male grooming products   \n",
       "1     A learning platform that provides topic-based ...   \n",
       "2               It aims to make learning fun via games.   \n",
       "3                 Provides interior designing solutions   \n",
       "4     It is a seed company engaged in production, pr...   \n",
       "...                                                 ...   \n",
       "2345                  A gig/on-demand staffing company.   \n",
       "2346  VAHDAM is among the world’s first vertically i...   \n",
       "2347  International education loans for high potenti...   \n",
       "2348  Collegedekho.com is Student’s Partner, Friend ...   \n",
       "2349  India’s first socially distributed full stack ...   \n",
       "\n",
       "                                   Founders  \\\n",
       "0                        Shantanu Deshpande   \n",
       "1     Adamas Belva Syah Devara, Iman Usman.   \n",
       "2                             Jatin Solanki   \n",
       "3              Srikanth Iyer, Rama Harinath   \n",
       "4                    Narayana Reddy Punyala   \n",
       "...                                     ...   \n",
       "2345            Chirag Mittal, Anirudh Syal   \n",
       "2346                             Bala Sarda   \n",
       "2347             Arnav Kumar, Vaibhav Singh   \n",
       "2348                           Ruchir Arora   \n",
       "2349          Vishal Chopra, Himanshu Gupta   \n",
       "\n",
       "                                              Investors       Amount  \\\n",
       "0                                  Sixth Sense Ventures    6300000.0   \n",
       "1                                      General Atlantic  150000000.0   \n",
       "2        Deepak Parekh, Amitabh Bachchan, Piyush Pandey   28000000.0   \n",
       "3     Evolvence India Fund (EIF), Pidilite Group, FJ...   30000000.0   \n",
       "4              Innovation in Food and Agriculture (IFA)    6000000.0   \n",
       "...                                                 ...          ...   \n",
       "2345                                    Endiya Partners    3000000.0   \n",
       "2346                                           IIFL AMC   20000000.0   \n",
       "2347                                       Owl Ventures   55000000.0   \n",
       "2348                   Winter Capital, ETS, Man Capital   26000000.0   \n",
       "2349                     3one4 Capital, Kalaari Capital    8000000.0   \n",
       "\n",
       "      Funding Stage Funding Year  \n",
       "0               NaN         2019  \n",
       "1          Series C         2019  \n",
       "2     Fresh funding         2019  \n",
       "3          Series D         2019  \n",
       "4               NaN         2019  \n",
       "...             ...          ...  \n",
       "2345   Pre-series A         2021  \n",
       "2346       Series D         2021  \n",
       "2347       Series C         2021  \n",
       "2348       Series B         2021  \n",
       "2349       Series A         2021  \n",
       "\n",
       "[2350 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining the DataFrames with similar column names\n",
    "combined_19_21 = pd.concat([data_19, data_20, data_21], ignore_index = True)\n",
    "combined_19_21.columns = [\"Company Name\", \"Year Founded\", \"Headquarters\", \"Sector\", \"Description\", \"Founders\", \"Investors\", \"Amount\", \"Funding Stage\", \"Funding Year\"]\n",
    "combined_19_21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2f2a5e64",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Seed</td>\n",
       "      <td>584000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit</td>\n",
       "      <td>Series A</td>\n",
       "      <td>949000.0</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Udaan</td>\n",
       "      <td>B2B</td>\n",
       "      <td>Series C</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Udaan is a B2B trade platform, designed specif...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Happyeasygo Group</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>Series A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>HappyEasyGo is an online travel domain.</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Mombay</td>\n",
       "      <td>Food and Beverage</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mombay is a unique opportunity for housewives ...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Droni Tech</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Seed</td>\n",
       "      <td>511000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Droni Tech manufacture UAVs and develop softwa...</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Netmeds</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>Series C</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Welcome to India's most convenient pharmacy!</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name                  Sector Funding Stage       Amount  \\\n",
       "0      TheCollegeFever         Brand Marketing          Seed     250000.0   \n",
       "1      Happy Cow Dairy             Agriculture          Seed     584000.0   \n",
       "2           MyLoanCare                  Credit      Series A     949000.0   \n",
       "3          PayMe India      Financial Services         Angel    2000000.0   \n",
       "4             Eunimart    E-Commerce Platforms          Seed          NaN   \n",
       "..                 ...                     ...           ...          ...   \n",
       "521              Udaan                     B2B      Series C  225000000.0   \n",
       "522  Happyeasygo Group                 Tourism      Series A          NaN   \n",
       "523             Mombay       Food and Beverage          Seed       7500.0   \n",
       "524         Droni Tech  Information Technology          Seed     511000.0   \n",
       "525            Netmeds           Biotechnology      Series C   35000000.0   \n",
       "\n",
       "    Headquarters                                        Description  \\\n",
       "0      Bangalore  TheCollegeFever is a hub for fun, fiesta and f...   \n",
       "1         Mumbai  A startup which aggregates milk from dairy far...   \n",
       "2        Gurgaon          Leading Online Loans Marketplace in India   \n",
       "3          Noida  PayMe India is an innovative FinTech organizat...   \n",
       "4      Hyderabad  Eunimart is a one stop solution for merchants ...   \n",
       "..           ...                                                ...   \n",
       "521    Bangalore  Udaan is a B2B trade platform, designed specif...   \n",
       "522      Haryana            HappyEasyGo is an online travel domain.   \n",
       "523       Mumbai  Mombay is a unique opportunity for housewives ...   \n",
       "524       Mumbai  Droni Tech manufacture UAVs and develop softwa...   \n",
       "525      Chennai       Welcome to India's most convenient pharmacy!   \n",
       "\n",
       "    Funding Year  \n",
       "0           2018  \n",
       "1           2018  \n",
       "2           2018  \n",
       "3           2018  \n",
       "4           2018  \n",
       "..           ...  \n",
       "521         2018  \n",
       "522         2018  \n",
       "523         2018  \n",
       "524         2018  \n",
       "525         2018  \n",
       "\n",
       "[526 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming the columns in the 2018 dataframe to match with the other dataframes\n",
    "data_18.columns = ['Company Name', 'Sector', 'Funding Stage', 'Amount', 'Headquarters', 'Description', \"Funding Year\"]\n",
    "data_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3a4505dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2876 entries, 0 to 2875\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company Name   2876 non-null   object \n",
      " 1   Sector         2858 non-null   object \n",
      " 2   Funding Stage  1945 non-null   object \n",
      " 3   Amount         2466 non-null   float64\n",
      " 4   Headquarters   2856 non-null   object \n",
      " 5   Description    2876 non-null   object \n",
      " 6   Funding Year   2876 non-null   object \n",
      " 7   Year Founded   2108 non-null   object \n",
      " 8   Founders       2331 non-null   object \n",
      " 9   Investors      2248 non-null   object \n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 224.8+ KB\n",
      "None \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Seed</td>\n",
       "      <td>584000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit</td>\n",
       "      <td>Series A</td>\n",
       "      <td>949000.0</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms</td>\n",
       "      <td>Seed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2871</th>\n",
       "      <td>Gigforce</td>\n",
       "      <td>Staffing &amp; Recruiting</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>A gig/on-demand staffing company.</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Chirag Mittal, Anirudh Syal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>Vahdam</td>\n",
       "      <td>Food &amp; Beverages</td>\n",
       "      <td>Series D</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>VAHDAM is among the world’s first vertically i...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Bala Sarda</td>\n",
       "      <td>IIFL AMC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>Leap Finance</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Series C</td>\n",
       "      <td>55000000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>International education loans for high potenti...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Arnav Kumar, Vaibhav Singh</td>\n",
       "      <td>Owl Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>CollegeDekho</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Series B</td>\n",
       "      <td>26000000.0</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Collegedekho.com is Student’s Partner, Friend ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Ruchir Arora</td>\n",
       "      <td>Winter Capital, ETS, Man Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>WeRize</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Series A</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Vishal Chopra, Himanshu Gupta</td>\n",
       "      <td>3one4 Capital, Kalaari Capital</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2876 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Company Name                 Sector Funding Stage      Amount  \\\n",
       "0     TheCollegeFever        Brand Marketing          Seed    250000.0   \n",
       "1     Happy Cow Dairy            Agriculture          Seed    584000.0   \n",
       "2          MyLoanCare                 Credit      Series A    949000.0   \n",
       "3         PayMe India     Financial Services         Angel   2000000.0   \n",
       "4            Eunimart   E-Commerce Platforms          Seed         NaN   \n",
       "...               ...                    ...           ...         ...   \n",
       "2871         Gigforce  Staffing & Recruiting  Pre-series A   3000000.0   \n",
       "2872           Vahdam       Food & Beverages      Series D  20000000.0   \n",
       "2873     Leap Finance     Financial Services      Series C  55000000.0   \n",
       "2874     CollegeDekho                 EdTech      Series B  26000000.0   \n",
       "2875           WeRize     Financial Services      Series A   8000000.0   \n",
       "\n",
       "     Headquarters                                        Description  \\\n",
       "0       Bangalore  TheCollegeFever is a hub for fun, fiesta and f...   \n",
       "1          Mumbai  A startup which aggregates milk from dairy far...   \n",
       "2         Gurgaon          Leading Online Loans Marketplace in India   \n",
       "3           Noida  PayMe India is an innovative FinTech organizat...   \n",
       "4       Hyderabad  Eunimart is a one stop solution for merchants ...   \n",
       "...           ...                                                ...   \n",
       "2871     Gurugram                  A gig/on-demand staffing company.   \n",
       "2872    New Delhi  VAHDAM is among the world’s first vertically i...   \n",
       "2873    Bangalore  International education loans for high potenti...   \n",
       "2874     Gurugram  Collegedekho.com is Student’s Partner, Friend ...   \n",
       "2875    Bangalore  India’s first socially distributed full stack ...   \n",
       "\n",
       "     Funding Year Year Founded                       Founders  \\\n",
       "0            2018          NaN                            NaN   \n",
       "1            2018          NaN                            NaN   \n",
       "2            2018          NaN                            NaN   \n",
       "3            2018          NaN                            NaN   \n",
       "4            2018          NaN                            NaN   \n",
       "...           ...          ...                            ...   \n",
       "2871         2021       2019.0    Chirag Mittal, Anirudh Syal   \n",
       "2872         2021       2015.0                     Bala Sarda   \n",
       "2873         2021       2019.0     Arnav Kumar, Vaibhav Singh   \n",
       "2874         2021       2015.0                   Ruchir Arora   \n",
       "2875         2021       2019.0  Vishal Chopra, Himanshu Gupta   \n",
       "\n",
       "                             Investors  \n",
       "0                                  NaN  \n",
       "1                                  NaN  \n",
       "2                                  NaN  \n",
       "3                                  NaN  \n",
       "4                                  NaN  \n",
       "...                                ...  \n",
       "2871                   Endiya Partners  \n",
       "2872                          IIFL AMC  \n",
       "2873                      Owl Ventures  \n",
       "2874  Winter Capital, ETS, Man Capital  \n",
       "2875    3one4 Capital, Kalaari Capital  \n",
       "\n",
       "[2876 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining the 2018 DataFrame to the 2019-2021 DataFrame\n",
    "complete_set = pd.concat([data_18, combined_19_21], ignore_index = True)\n",
    "print(complete_set.info(), \"\\n\")\n",
    "complete_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8d13140f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Company Name     2214\n",
      "Sector            595\n",
      "Funding Stage      71\n",
      "Amount            512\n",
      "Headquarters      134\n",
      "Description      2690\n",
      "Funding Year        4\n",
      "Year Founded       58\n",
      "Founders         1979\n",
      "Investors        1771\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TheCollegeFever</td>\n",
       "      <td>Brand Marketing</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy Cow Dairy</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Seed</td>\n",
       "      <td>584000</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MyLoanCare</td>\n",
       "      <td>Credit</td>\n",
       "      <td>Series A</td>\n",
       "      <td>949000</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PayMe India</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-Commerce Platforms</td>\n",
       "      <td>Seed</td>\n",
       "      <td>0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>Gigforce</td>\n",
       "      <td>Staffing &amp; Recruiting</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>3000000</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>A gig/on-demand staffing company.</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Chirag Mittal, Anirudh Syal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>Vahdam</td>\n",
       "      <td>Food &amp; Beverages</td>\n",
       "      <td>Series D</td>\n",
       "      <td>20000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>VAHDAM is among the world’s first vertically i...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Bala Sarda</td>\n",
       "      <td>IIFL AMC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>Leap Finance</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Series C</td>\n",
       "      <td>55000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>International education loans for high potenti...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Arnav Kumar, Vaibhav Singh</td>\n",
       "      <td>Owl Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>CollegeDekho</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Series B</td>\n",
       "      <td>26000000</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Collegedekho.com is Student’s Partner, Friend ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Ruchir Arora</td>\n",
       "      <td>Winter Capital, ETS, Man Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>WeRize</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Series A</td>\n",
       "      <td>8000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>Vishal Chopra, Himanshu Gupta</td>\n",
       "      <td>3one4 Capital, Kalaari Capital</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2855 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Company Name                 Sector Funding Stage    Amount  \\\n",
       "0     TheCollegeFever        Brand Marketing          Seed    250000   \n",
       "1     Happy Cow Dairy            Agriculture          Seed    584000   \n",
       "2          MyLoanCare                 Credit      Series A    949000   \n",
       "3         PayMe India     Financial Services         Angel   2000000   \n",
       "4            Eunimart   E-Commerce Platforms          Seed         0   \n",
       "...               ...                    ...           ...       ...   \n",
       "2850         Gigforce  Staffing & Recruiting  Pre-series A   3000000   \n",
       "2851           Vahdam       Food & Beverages      Series D  20000000   \n",
       "2852     Leap Finance     Financial Services      Series C  55000000   \n",
       "2853     CollegeDekho                 EdTech      Series B  26000000   \n",
       "2854           WeRize     Financial Services      Series A   8000000   \n",
       "\n",
       "     Headquarters                                        Description  \\\n",
       "0       Bangalore  TheCollegeFever is a hub for fun, fiesta and f...   \n",
       "1          Mumbai  A startup which aggregates milk from dairy far...   \n",
       "2         Gurgaon          Leading Online Loans Marketplace in India   \n",
       "3           Noida  PayMe India is an innovative FinTech organizat...   \n",
       "4       Hyderabad  Eunimart is a one stop solution for merchants ...   \n",
       "...           ...                                                ...   \n",
       "2850     Gurugram                  A gig/on-demand staffing company.   \n",
       "2851    New Delhi  VAHDAM is among the world’s first vertically i...   \n",
       "2852    Bangalore  International education loans for high potenti...   \n",
       "2853     Gurugram  Collegedekho.com is Student’s Partner, Friend ...   \n",
       "2854    Bangalore  India’s first socially distributed full stack ...   \n",
       "\n",
       "     Funding Year Year Founded                       Founders  \\\n",
       "0            2018          nan                            NaN   \n",
       "1            2018          nan                            NaN   \n",
       "2            2018          nan                            NaN   \n",
       "3            2018          nan                            NaN   \n",
       "4            2018          nan                            NaN   \n",
       "...           ...          ...                            ...   \n",
       "2850         2021       2019.0    Chirag Mittal, Anirudh Syal   \n",
       "2851         2021       2015.0                     Bala Sarda   \n",
       "2852         2021       2019.0     Arnav Kumar, Vaibhav Singh   \n",
       "2853         2021       2015.0                   Ruchir Arora   \n",
       "2854         2021       2019.0  Vishal Chopra, Himanshu Gupta   \n",
       "\n",
       "                             Investors  \n",
       "0                                  NaN  \n",
       "1                                  NaN  \n",
       "2                                  NaN  \n",
       "3                                  NaN  \n",
       "4                                  NaN  \n",
       "...                                ...  \n",
       "2850                   Endiya Partners  \n",
       "2851                          IIFL AMC  \n",
       "2852                      Owl Ventures  \n",
       "2853  Winter Capital, ETS, Man Capital  \n",
       "2854    3one4 Capital, Kalaari Capital  \n",
       "\n",
       "[2855 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting various columns into appropriate formats\n",
    "complete_set['Amount'] = complete_set['Amount'].replace(np.nan, 0)\n",
    "complete_set['Amount'] = complete_set['Amount'].apply(int)\n",
    "complete_set[\"Funding Year\"] = complete_set[\"Funding Year\"].apply(str)\n",
    "complete_set[\"Year Founded\"] = complete_set[\"Year Founded\"].apply(str)\n",
    "\n",
    "# Dropping all duplicates from the combined DataFrame\n",
    "complete_set.drop_duplicates(inplace = True)\n",
    "complete_set.reset_index(drop=True, inplace = True)\n",
    "\n",
    "# Taking a final look at the Complete Set\n",
    "print(complete_set.nunique(), \"\\n\")\n",
    "complete_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be2a6972",
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "# Exporting to have a look at the DataFrame in Excel\n",
    "complete_set.to_csv(\"complete_set.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b41f5c4",
   "metadata": {},
   "source": [
    "### 3.8 Checking the integrity of the combined DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a7a20b9",
   "metadata": {},
   "source": [
    "#### 3.8.1 Company Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef87b6cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BharatPe                        10\n",
       "Zomato                           7\n",
       "Nykaa                            6\n",
       "Zetwerk                          6\n",
       "MPL                              6\n",
       "                                ..\n",
       "Genrobotics                      1\n",
       "Reliance Retail Ventures Ltd     1\n",
       "Chargebee                        1\n",
       "Credit Wise Capital              1\n",
       "WeRize                           1\n",
       "Name: Company Name, Length: 2214, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_companies = (complete_set.loc[:,\"Company Name\"]).value_counts()\n",
    "unique_companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1115c27e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Byju's      10\n",
       "Bharatpe    10\n",
       "Zomato       7\n",
       "Nykaa        6\n",
       "Trell        6\n",
       "Zetwerk      6\n",
       "Vedantu      6\n",
       "Oyo          6\n",
       "Mpl          6\n",
       "Spinny       6\n",
       "Name: Company Name, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Capitalizing only the first letters of each entry in the column for normalization\n",
    "complete_set[\"Company Name\"] = complete_set[\"Company Name\"].apply(lambda x: str(x).capitalize())\n",
    "\n",
    "# Correcting the misspelt names of startups\n",
    "complete_set.loc[complete_set[\"Company Name\"] == \"Byju\", \"Company Name\"] = \"Byju's\"\n",
    "\n",
    "# Reassigning the dataframe and previewing it\n",
    "unique_companies = (complete_set.loc[:,\"Company Name\"]).value_counts()\n",
    "unique_companies.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "31406caa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_companies.head(10).sort_values().plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b251e70c",
   "metadata": {},
   "source": [
    "From here, we note that Bharatpe (10), Byju's (10) and Zomato (7) were the startups involved in most deals over the period. Let's take a closer look at them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e6b5a538",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food Delivery</td>\n",
       "      <td>Secondary Market</td>\n",
       "      <td>210000000</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Zomato is a search app offering detailed infor...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2019</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>South Africa’s Naspers Ventures, the CPP Inves...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food Delivery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>660000000</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Zomato is a food delivery and restaurant disco...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal, Gaurav Gupta, Pankaj Chaddah</td>\n",
       "      <td>Kora, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran, Divya Gokulnath</td>\n",
       "      <td>Owl Ventures, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>FoodTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52000000</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Zomato is a food delivery and restaurant disco...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal, Gaurav Gupta, Pankaj Chaddah</td>\n",
       "      <td>Kora, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food devlivery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62000000</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Get online food delivery from restaurants near...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal, Pankaj Chaddah</td>\n",
       "      <td>MacRitchie Investments, Baillie Gifford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food devlivery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000000</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Get online food delivery from restaurants near...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal, Pankaj Chaddah</td>\n",
       "      <td>MacRitchie Investments, Baillie Gifford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>DST Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>BOND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Foodtech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Gurugram</td>\n",
       "      <td>Online food delivery and restaurant discovery ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>Pacific Horizon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series C</td>\n",
       "      <td>75000000</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Digital payments platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Ribbit Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>General Atlantic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000000</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Digital payments platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Coatue Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Foodtech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>Online food delivery and restaurant discovery ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>Ant Financial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Edtech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Tiger Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>20000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>The vision to make financial inclusion a reali...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>IIFL Wealth, Asset Management, Northern Arc Ca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Series E</td>\n",
       "      <td>370000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe launched India’s first UPI interopera...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>Tiger Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Series D</td>\n",
       "      <td>100000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>Amplo, Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>6000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>InnoVen Capital, Steadview Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>10000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>InnoVen Capital, Steadview Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>8200000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>Steadview Capital, Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Byju Raveendran, Divya Gokulnath</td>\n",
       "      <td>Tiga Investments, Baron Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>Series F</td>\n",
       "      <td>460000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Byju Raveendran, Divya Gokulnath</td>\n",
       "      <td>BlackRock, Owl Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>6000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe is the definitive platform for financ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Bhavik Koladiya, Shashvat Nakrani</td>\n",
       "      <td>Amplo, Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2508</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Byju Raveendran, Divya Gokulnath</td>\n",
       "      <td>Tiga Investments, Baron Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2597</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>EdTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000000</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU’S is India's largest ed-tech company and ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Byju Raveendran, Divya Gokulnath</td>\n",
       "      <td>Oxshott Capital Partners, XN Exponent, Edelweiss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2677</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000000</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe launched India’s first UPI interopera...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Ashneer Grover, Shashvat Nakrani</td>\n",
       "      <td>MAS Financial Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company Name              Sector     Funding Stage     Amount  \\\n",
       "443        Zomato       Food Delivery  Secondary Market  210000000   \n",
       "541        Byju's              Edtech               NaN  540000000   \n",
       "629        Zomato       Food Delivery               NaN  660000000   \n",
       "738        Byju's              EdTech               NaN  200000000   \n",
       "859        Zomato            FoodTech               NaN   52000000   \n",
       "960        Zomato      Food devlivery               NaN   62000000   \n",
       "978        Zomato      Food devlivery               NaN  100000000   \n",
       "1104       Byju's              EdTech               NaN  122000000   \n",
       "1228       Byju's              Edtech               NaN          0   \n",
       "1411       Zomato            Foodtech               NaN    5000000   \n",
       "1498     Bharatpe             Fintech          Series C   75000000   \n",
       "1545       Byju's              Edtech               NaN  200000000   \n",
       "1571     Bharatpe             Fintech               NaN  100000000   \n",
       "1644       Zomato            Foodtech               NaN  150000000   \n",
       "1645       Byju's              Edtech               NaN  200000000   \n",
       "1804     Bharatpe             FinTech              Debt   20000000   \n",
       "1846     Bharatpe             FinTech          Series E  370000000   \n",
       "1985     Bharatpe             FinTech          Series D  100000000   \n",
       "2047     Bharatpe             FinTech              Debt    6000000   \n",
       "2053     Bharatpe             FinTech              Debt   10000000   \n",
       "2093     Bharatpe             FinTech              Debt    8200000   \n",
       "2284       Byju's              EdTech               NaN  350000000   \n",
       "2382       Byju's              EdTech          Series F  460000000   \n",
       "2493     Bharatpe             FinTech              Debt    6000000   \n",
       "2508       Byju's              EdTech               NaN  150000000   \n",
       "2597       Byju's              EdTech               NaN  300000000   \n",
       "2677     Bharatpe  Financial Services               NaN   10000000   \n",
       "\n",
       "     Headquarters                                        Description  \\\n",
       "443       Gurgaon  Zomato is a search app offering detailed infor...   \n",
       "541           NaN                   Provides online learning classes   \n",
       "629       Gurgaon  Zomato is a food delivery and restaurant disco...   \n",
       "738     Bangalore  BYJU'S is an educational technology company th...   \n",
       "859      Gurugram  Zomato is a food delivery and restaurant disco...   \n",
       "960       Haryana  Get online food delivery from restaurants near...   \n",
       "978       Haryana  Get online food delivery from restaurants near...   \n",
       "1104    Bangalore                   Provides online learning classes   \n",
       "1228    Bangalore                   Provides online learning classes   \n",
       "1411     Gurugram  Online food delivery and restaurant discovery ...   \n",
       "1498        Delhi                          Digital payments platform   \n",
       "1545          nan                   Provides online learning classes   \n",
       "1571        Delhi                          Digital payments platform   \n",
       "1644          nan  Online food delivery and restaurant discovery ...   \n",
       "1645          nan                   Provides online learning classes   \n",
       "1804    New Delhi  The vision to make financial inclusion a reali...   \n",
       "1846    New Delhi  BharatPe launched India’s first UPI interopera...   \n",
       "1985    New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2047    New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2053    New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2093    New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2284    Bangalore  BYJU'S is an educational technology company th...   \n",
       "2382    Bangalore  BYJU'S is an educational technology company th...   \n",
       "2493    New Delhi  BharatPe is the definitive platform for financ...   \n",
       "2508    Bangalore  BYJU'S is an educational technology company th...   \n",
       "2597    Bangalore  BYJU’S is India's largest ed-tech company and ...   \n",
       "2677    New Delhi  BharatPe launched India’s first UPI interopera...   \n",
       "\n",
       "     Funding Year Year Founded  \\\n",
       "443          2018          nan   \n",
       "541          2019       2011.0   \n",
       "629          2020         2008   \n",
       "738          2020         2011   \n",
       "859          2020         2008   \n",
       "960          2020         2008   \n",
       "978          2020         2008   \n",
       "1104         2020         2011   \n",
       "1228         2020         2011   \n",
       "1411         2020          nan   \n",
       "1498         2020         2018   \n",
       "1545         2020         2011   \n",
       "1571         2020         2018   \n",
       "1644         2020          nan   \n",
       "1645         2020         2011   \n",
       "1804         2021       2018.0   \n",
       "1846         2021       2018.0   \n",
       "1985         2021       2018.0   \n",
       "2047         2021       2018.0   \n",
       "2053         2021       2018.0   \n",
       "2093         2021       2018.0   \n",
       "2284         2021       2011.0   \n",
       "2382         2021       2011.0   \n",
       "2493         2021       2018.0   \n",
       "2508         2021       2011.0   \n",
       "2597         2021       2011.0   \n",
       "2677         2021       2018.0   \n",
       "\n",
       "                                               Founders  \\\n",
       "443                                                 NaN   \n",
       "541                                     Byju Raveendran   \n",
       "629       Deepinder Goyal, Gaurav Gupta, Pankaj Chaddah   \n",
       "738                    Byju Raveendran, Divya Gokulnath   \n",
       "859       Deepinder Goyal, Gaurav Gupta, Pankaj Chaddah   \n",
       "960                     Deepinder Goyal, Pankaj Chaddah   \n",
       "978                     Deepinder Goyal, Pankaj Chaddah   \n",
       "1104                                    Byju Raveendran   \n",
       "1228                                    Byju Raveendran   \n",
       "1411                                    Deepinder Goyal   \n",
       "1498                                     Ashneer Grover   \n",
       "1545                                    Byju Raveendran   \n",
       "1571                                     Ashneer Grover   \n",
       "1644                                    Deepinder Goyal   \n",
       "1645                                    Byju Raveendran   \n",
       "1804                   Ashneer Grover, Shashvat Nakrani   \n",
       "1846                   Ashneer Grover, Shashvat Nakrani   \n",
       "1985                   Ashneer Grover, Shashvat Nakrani   \n",
       "2047                   Ashneer Grover, Shashvat Nakrani   \n",
       "2053                   Ashneer Grover, Shashvat Nakrani   \n",
       "2093                   Ashneer Grover, Shashvat Nakrani   \n",
       "2284                   Byju Raveendran, Divya Gokulnath   \n",
       "2382                   Byju Raveendran, Divya Gokulnath   \n",
       "2493  Ashneer Grover, Bhavik Koladiya, Shashvat Nakrani   \n",
       "2508                   Byju Raveendran, Divya Gokulnath   \n",
       "2597                   Byju Raveendran, Divya Gokulnath   \n",
       "2677                   Ashneer Grover, Shashvat Nakrani   \n",
       "\n",
       "                                              Investors  \n",
       "443                                                 NaN  \n",
       "541   South Africa’s Naspers Ventures, the CPP Inves...  \n",
       "629                       Kora, Tiger Global Management  \n",
       "738               Owl Ventures, Tiger Global Management  \n",
       "859                       Kora, Tiger Global Management  \n",
       "960             MacRitchie Investments, Baillie Gifford  \n",
       "978             MacRitchie Investments, Baillie Gifford  \n",
       "1104                                         DST Global  \n",
       "1228                                               BOND  \n",
       "1411                                    Pacific Horizon  \n",
       "1498                                     Ribbit Capital  \n",
       "1545                                   General Atlantic  \n",
       "1571                                  Coatue Management  \n",
       "1644                                      Ant Financial  \n",
       "1645                                       Tiger Global  \n",
       "1804  IIFL Wealth, Asset Management, Northern Arc Ca...  \n",
       "1846                                       Tiger Global  \n",
       "1985                            Amplo, Insight Partners  \n",
       "2047                 InnoVen Capital, Steadview Capital  \n",
       "2053                 InnoVen Capital, Steadview Capital  \n",
       "2093                Steadview Capital, Insight Partners  \n",
       "2284                    Tiga Investments, Baron Capital  \n",
       "2382                            BlackRock, Owl Ventures  \n",
       "2493                            Amplo, Insight Partners  \n",
       "2508                    Tiga Investments, Baron Capital  \n",
       "2597   Oxshott Capital Partners, XN Exponent, Edelweiss  \n",
       "2677                             MAS Financial Services  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set.loc[(complete_set[\"Company Name\"] == \"Bharatpe\") | \n",
    "                 (complete_set[\"Company Name\"] == \"Byju's\") | \n",
    "                 (complete_set[\"Company Name\"] == \"Zomato\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a4c474",
   "metadata": {},
   "source": [
    "We note from the above that the top 3 startups involved in most funding deals are located in 3 different locations and operate in 3 different sectors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a3239a",
   "metadata": {},
   "source": [
    "#### 3.8.2 Sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "26d4395c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FinTech                             174\n",
       "EdTech                              147\n",
       "Financial Services                   86\n",
       "Fintech                              85\n",
       "Edtech                               74\n",
       "                                   ... \n",
       "Startup laboratory                    1\n",
       "Online credit management startup      1\n",
       "Battery design                        1\n",
       "Eyeglasses                            1\n",
       "Staffing & Recruiting                 1\n",
       "Name: Sector, Length: 595, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_sectors = (complete_set.loc[:,\"Sector\"]).value_counts()\n",
    "unique_sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7e7b3b5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Capitalizing only the first letters of each entry in the Sector column for normalization\n",
    "complete_set[\"Sector\"] = complete_set[\"Sector\"].apply(lambda x: str(x).capitalize())\n",
    "\n",
    "# Equating similar entries in the Sector column for ease of analysis\n",
    "complete_set[\"Sector\"] = complete_set[\"Sector\"].apply(lambda x: str(x).replace(\" and \", \" & \"))\n",
    "complete_set[\"Sector\"] = complete_set[\"Sector\"].apply(lambda x: str(x).replace(\"startup\", \"\"))\n",
    "complete_set[\"Sector\"] = complete_set[\"Sector\"].apply(lambda x: str(x).replace(\"  \", \" \"))\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"nan\", \"Sector\"] = \"Sector TBD\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"â€”\", \"Sector\"] = \"Sector TBD\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d1af09de",
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# Equating similar entries in the Sector column for ease of analysis (2)\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Accomodation\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Accounting\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ad-tech\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Advertisement\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Advertising\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Advisory firm\", \"Sector\"] = \"Advisory & consultancy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Aeorspace\", \"Sector\"] = \"Aviation & aerospace\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Aero company\", \"Sector\"] = \"Aviation & aerospace\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Aerospace\", \"Sector\"] = \"Aviation & aerospace\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Agri tech\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Agriculture\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Agritech\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Agritech/commerce\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Agtech\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai & data science\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai & debt\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai & deep learning\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai & media\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai & tech\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai chatbot\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai company\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai health\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai platform\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai robotics\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ai\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Air transportation\", \"Sector\"] = \"Aviation & aerospace\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Alternative medicine\", \"Sector\"] = \"Medical\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Analytics\", \"Sector\"] = \"Data science & analytics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Appliance\", \"Sector\"] = \"Appliances & Electronics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Apps\", \"Sector\"] = \"Software\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ar platform\", \"Sector\"] = \"Ar/vr\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ar\", \"Sector\"] = \"Ar/vr\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ar/vr\", \"Sector\"] = \"Ar/vr\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Artificial intelligence\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Audio\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Augmented reality\", \"Sector\"] = \"Ar/vr\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Auto-tech\", \"Sector\"] = \"Automation tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automation\", \"Sector\"] = \"Automation tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automobile & technology\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automobile technology\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automobile\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automobiles\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automotive & rentals\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automotive and rentals\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automotive company\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automotive tech\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Automotive\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Autonomous vehicles\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Aviation\", \"Sector\"] = \"Aviation & aerospace\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ayurveda tech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b agritech\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b e-commerce\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b ecommerce\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b manufacturing\", \"Sector\"] = \"Manufacturing\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b marketplace\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b service\", \"Sector\"] = \"B2b\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b supply chain\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b travel\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"B2b\", \"Sector\"] = \"B2b\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Banking\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Battery design\", \"Sector\"] = \"Battery\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Battery manufacturer\", \"Sector\"] = \"Battery\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Beauty & wellness\", \"Sector\"] = \"Beauty\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Beauty products\", \"Sector\"] = \"Beauty\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Beverage\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Beverage\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Beverages\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Big data\", \"Sector\"] = \"Data science & analytics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Bike marketplace\", \"Sector\"] = \"Bike services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Bike rental\", \"Sector\"] = \"Bike services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Biopharma\", \"Sector\"] = \"Pharmaceutical\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Biotech\", \"Sector\"] = \"Biotechnology\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Biotechnology company\", \"Sector\"] = \"Biotechnology\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Biotechnology\", \"Sector\"] = \"Biotechnology\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Blockchain\", \"Sector\"] = \"Cryptocurrency\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Blogging\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Brand marketing\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Broadcasting\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Business development\", \"Sector\"] = \"Advisory & consultancy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Business intelligence\", \"Sector\"] = \"Data science & analytics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Business travel\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cannabis\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Capital markets\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Car service\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Car trade\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Catering\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Children\", \"Sector\"] = \"Child care\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Classifieds\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Clean energy\", \"Sector\"] = \"Clean energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Clean energy\", \"Sector\"] = \"Energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cleantech\", \"Sector\"] = \"Clean energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cleantech\", \"Sector\"] = \"Energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Clothing\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cloud company\", \"Sector\"] = \"Cloud computing\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cloud infrastructure\", \"Sector\"] = \"Cloud computing\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Commerce\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Commercial real estate\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Commercial\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Communities\", \"Sector\"] = \"Social media & communities\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Communities\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Community platform\", \"Sector\"] = \"Social media & communities\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Community platform\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Community\", \"Sector\"] = \"Social media & communities\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Community\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Computer games\", \"Sector\"] = \"Games\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Computer software\", \"Sector\"] = \"Software\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Construction tech\", \"Sector\"] = \"Construction\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consultancy\", \"Sector\"] = \"Advisory & consultancy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consulting\", \"Sector\"] = \"Advisory & consultancy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer appliances\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer applications\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer electronics\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer goods\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer lending\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer service\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer services\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer software\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer software\", \"Sector\"] = \"Software\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Consumer\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Content commerce\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Content creation\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Content management\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Content marketplace\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Content marktplace\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Content publishing\", \"Sector\"] = \"Content services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Continuing education\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Conversational ai platform\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cooking\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cosmetics\", \"Sector\"] = \"Beauty\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Coworking\", \"Sector\"] = \"Co-working\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Creative agency\", \"Sector\"] = \"Arts & crafts\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Credit cards\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Credit\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Crm\", \"Sector\"] = \"Customer service\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Crypto\", \"Sector\"] = \"Cryptocurrency\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Customer service company\", \"Sector\"] = \"Customer service\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Cybersecurity\", \"Sector\"] = \"Computer & network security\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"D2c business\", \"Sector\"] = \"D2c\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"D2c fashion\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"D2c jewellery\", \"Sector\"] = \"Jewellery\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Dairy\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Data analytics\", \"Sector\"] = \"Data science & analytics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Data intelligence\", \"Sector\"] = \"Data science & analytics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Data science\", \"Sector\"] = \"Data science & analytics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Dating app\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Dating\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Deep tech ai\", \"Sector\"] = \"Deep Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Deep tech\", \"Sector\"] = \"Deep Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Deeptech\", \"Sector\"] = \"Deep Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Defense & space\", \"Sector\"] = \"Defense\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Defense tech\", \"Sector\"] = \"Defense\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Deisgning\", \"Sector\"] = \"Design\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Delivery service\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Delivery\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Dental\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Design\", \"Sector\"] = \"\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Dietary supplements\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Digital entertainment\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Digital marketing\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Digital media\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Digital mortgage\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E store\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-commerce & ar\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-commerce platforms\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-commerce\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-learning\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-market\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-marketplace\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-mobility\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-sports\", \"Sector\"] = \"Sports\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"E-tail\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ecommerce\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Edtech\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Edttech\", \"Sector\"] = \"Edtech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Edttech\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Education management\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Electricity\", \"Sector\"] = \"Energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Electronics\", \"Sector\"] = \"Appliances & Electronics\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Emobility\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Energy\", \"Sector\"] = \"Energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Enterprise software\", \"Sector\"] = \"Enterprise resource planning (erp)\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Environmental consulting\", \"Sector\"] = \"Environmental services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Environmental service\", \"Sector\"] = \"Environmental services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Equity management\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Esports\", \"Sector\"] = \"Sports\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Estore\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Ev\", \"Sector\"] = \"Electric vehicle\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Events\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Eye wear\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Eyeglasses\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Eyewear\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Facilities support service\", \"Sector\"] = \"Facilities services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fantasy sports\", \"Sector\"] = \"Sports\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Farming\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fashion & lifestyle\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fashion tech\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fashion\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Femtech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fertility tech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Finance company\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Finance\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Financial services\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fintech\", \"Sector\"] = \"Fintech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fishery\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fitness\", \"Sector\"] = \"Personal care\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fmcg\", \"Sector\"] = \"Consumer goods & services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & bevarages\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & beverage\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & beverages\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & logistics\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & nutrition\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & Nutrition\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food & tech\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food delivery\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food devlivery\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food diet\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food industry\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food processing\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food production\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food tech\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Food\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Foodtech & logistics\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Foodtech\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Foootwear\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Funding platform\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Furniture rental\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Furniture\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Fusion beverages\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Gaming\", \"Sector\"] = \"Games\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Healtcare\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health & fitness\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health & wellness\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health and fitness\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health care\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health diagnostics\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health insurance\", \"Sector\"] = \"Insurance\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Health, wellness & fitness\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Healthcare\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Healthcare/edtech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Healthtech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Heathcare\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Heathtech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Helathcare\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Higher education\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Home decor\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Home design\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Home interior services\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Home services\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hospital & health care\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hospital\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hospitality\", \"Sector\"] = \"Tourism & Hospitality\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Housing & rentals\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Housing marketplace\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Housing\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hr tech\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hr\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hrtech\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Human resources\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Hygiene management\", \"Sector\"] = \"Hygiene\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Information technology & services\", \"Sector\"] = \"Information technology\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Insurance tech\", \"Sector\"] = \"Insurance\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Insurance technology\", \"Sector\"] = \"Insurance\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Insuretech\", \"Sector\"] = \"Insurance\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Insurtech\", \"Sector\"] = \"Insurance\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Interior & decor\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Interior design\", \"Sector\"] = \"Furniture & Home Decor\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Internet of things\", \"Sector\"] = \"IoT\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Investment banking\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Investment management\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Investment tech\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Investment\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Iot platform\", \"Sector\"] = \"IoT\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Iot/automobile\", \"Sector\"] = \"IoT\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"It company\", \"Sector\"] = \"It\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Job discovery platform\", \"Sector\"] = \"Job search\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Job portal\", \"Sector\"] = \"Job search\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Last mile transportation\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Legal services\", \"Sector\"] = \"Legal\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Legal tech\", \"Sector\"] = \"Legal\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Legaltech\", \"Sector\"] = \"Legal\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Logistics\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Logitech\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Luxury car\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Management consulting\", \"Sector\"] = \"Advisory\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Market research\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Marketing & advertising\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Marketing & customer loyalty\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Marketing company\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Marketing\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Marketplace\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Martech\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Matrimony\", \"Sector\"] = \"Marriage\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mechanical & industrial engineering\", \"Sector\"] = \"Engineering\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mechanical or industrial engineering\", \"Sector\"] = \"Engineering\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Med tech\", \"Sector\"] = \"Medical\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Media & entertainment\", \"Sector\"] = \"Media\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Media & networking\", \"Sector\"] = \"Media\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Media and entertainment\", \"Sector\"] = \"Media\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Media tech\", \"Sector\"] = \"Media\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Medical device\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Medtech\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mental health\", \"Sector\"] = \"Medicine & healthcare\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Micro-mobiity\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Milk\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mlops platform\", \"Sector\"] = \"Machine learning\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mobile games\", \"Sector\"] = \"Games\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mobile games\", \"Sector\"] = \"Mobile\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mobile payments\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mobility tech\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mobility\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mobility/transport\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Music streaming\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Music\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Mutual funds\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Nano distribution network\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Neo-banking\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Networking platform\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Networking\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"News\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Nft marketplace\", \"Sector\"] = \"NFT\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Nft\", \"Sector\"] = \"NFT\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Nutrition sector\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Nutrition tech\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Nutrition\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Oil & energy\", \"Sector\"] = \"Energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Online credit management\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Online financial service\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Online games\", \"Sector\"] = \"Games\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Online media\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Online portals\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Packaging solution\", \"Sector\"] = \"Packaging services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Pet care\", \"Sector\"] = \"Animal Care\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Pharma\", \"Sector\"] = \"Pharmaceutical\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Pharmacy\", \"Sector\"] = \"Pharmaceutical\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Podcast\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Pollution control equiptment\", \"Sector\"] = \"Hygiene\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Preschool daycare\", \"Sector\"] = \"Education\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Professional training & coaching\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Publication\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Real estate\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Real Estate\", \"Sector\"] = \"Housing & real estate\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Reatil\", \"Sector\"] = \"Retail\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Recruitment\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Renewable player\", \"Sector\"] = \"Renewable energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Renewables & environment\", \"Sector\"] = \"Renewable energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Rental space\", \"Sector\"] = \"Rentals\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Rental\", \"Sector\"] = \"Rentals\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Retail aggregator\", \"Sector\"] = \"Retail\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Retail tech\", \"Sector\"] = \"Retail\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Robotics & ai\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Robotics\", \"Sector\"] = \"Ai\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Saas platform\", \"Sector\"] = \"SAAS\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \" Saas\", \"Sector\"] = \"SAAS\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Saas/edtech\", \"Sector\"] = \"SAAS\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"SaasÂ Â startup\", \"Sector\"] = \"SAAS\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sales & distribution\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sales & services\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sales and distribution\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sanitation solutions\", \"Sector\"] = \"Hygiene\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Skincare\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Skincare\", \"Sector\"] = \"Beauty\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sles & marketing\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sles and marketing\", \"Sector\"] = \"Advertising, marketing & sales\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social audio\", \"Sector\"] = \"Entertainment\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social commerce\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social community\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social e-commerce\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social media & communities\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social media\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social network\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Social platform\", \"Sector\"] = \"Social media & networking\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Software company\", \"Sector\"] = \"Software\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Soil-tech\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Solar monitoring company\", \"Sector\"] = \"Solar energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Solar saas\", \"Sector\"] = \"Solar energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Solar solution\", \"Sector\"] = \"Solar energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Solar\", \"Sector\"] = \"Solar energy\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Sportstech\", \"Sector\"] = \"Sports\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Staffing & recruiting\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Supply chain platform\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Supply chain, agritech\", \"Sector\"] = \"Logistics & supply chain\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Taxation\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Tech company\", \"Sector\"] = \"Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Tech hub\", \"Sector\"] = \"Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Tech platform\", \"Sector\"] = \"Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Technology\", \"Sector\"] = \"Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Techonology\", \"Sector\"] = \"Tech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Telecommuncation\", \"Sector\"] = \"Telecommunication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Telecommunications\", \"Sector\"] = \"Telecommunication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Textiles\", \"Sector\"] = \"Apparel & fashion\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Tobacco\", \"Sector\"] = \"Agriculture & agritech\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Tourism & ev\", \"Sector\"] = \"Tourism & Hospitality\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Tourism\", \"Sector\"] = \"Tourism & Hospitality\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Trading platform\", \"Sector\"] = \"E-commerce\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Training\", \"Sector\"] = \"Human Resources\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Transport & rentals\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Transport automation\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Transport\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Transportation\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Travel & saas\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Travel tech\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Travel\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Traveltech\", \"Sector\"] = \"Travel & Transport\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Vehicle repair\", \"Sector\"] = \"Automobiles & automotives\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Venture capital & private equity\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Venture capital\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Venture capitalist\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Veterinary\", \"Sector\"] = \"Animal Care\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Video personalization\", \"Sector\"] = \"Video communication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Video platform\", \"Sector\"] = \"Video communication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Video sharing platform\", \"Sector\"] = \"Video communication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Video streaming platform\", \"Sector\"] = \"Video communication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Video\", \"Sector\"] = \"Video communication\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Virtual auditing\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Virtual banking\", \"Sector\"] = \"Financial Services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Visual media\", \"Sector\"] = \"Information services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Vr & saas\", \"Sector\"] = \"Ar/vr\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Wealth management\", \"Sector\"] = \"Financial services\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Wedding\", \"Sector\"] = \"Marriage\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Wellness\", \"Sector\"] = \"Personal care\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Wine & spirits\", \"Sector\"] = \"Food & Nutrition\"\n",
    "complete_set.loc[complete_set[\"Sector\"] == \"Yoga & wellness\", \"Sector\"] = \"Personal care\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2441a8e2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education                    278\n",
       "Fintech                      259\n",
       "Medicine & healthcare        227\n",
       "E-commerce                   158\n",
       "Financial services           144\n",
       "Food & Nutrition             137\n",
       "Agriculture & agritech        85\n",
       "Automobiles & automotives     81\n",
       "Logistics & supply chain      70\n",
       "Consumer goods & services     62\n",
       "Name: Sector, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_sectors = (complete_set.loc[:,\"Sector\"]).value_counts()\n",
    "unique_sectors.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c0e5dfa2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Funding Deals per Sector')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_sectors.head(10).sort_values().plot.barh()\n",
    "plt.xlabel(\"Number of Funding Deals\")\n",
    "plt.ylabel(\"Sector\")\n",
    "plt.title(\"Funding Deals per Sector\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de621a76",
   "metadata": {},
   "source": [
    "Here, we note that startups in the education (279), fintech (258), and medicine & healthcare (227) sectors were involved in most transactions over the period, with financial services (166) and e-commerce (158) following in distance. The top 10 (out of 202) sectors, by number of deals, make up about 53% of the total deals over the period. It will therefore be fair to conclude that funding is centralized around a few sectors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba348e33",
   "metadata": {},
   "source": [
    "#### 3.8.3 Funding Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ef604cc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seed                        604\n",
       "Series A                    305\n",
       "Pre-series A                209\n",
       "Series B                    134\n",
       "Series C                    114\n",
       "Seed Round                   69\n",
       "Pre series A                 62\n",
       "Pre-seed                     58\n",
       "Series D                     51\n",
       "Debt                         45\n",
       "Angel                        38\n",
       "Venture - Series Unknown     37\n",
       "Series E                     29\n",
       "Pre-series B                 14\n",
       "Debt Financing               13\n",
       "Name: Funding Stage, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_stages = (complete_set.loc[:,\"Funding Stage\"]).value_counts()\n",
    "unique_stages.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f50402d",
   "metadata": {},
   "source": [
    "From the preview of the unique stages above, we see that a number of stages implying the same stage are grouped differently due to differences in capitalization, spacing and use of hyphens. They should therefore be standardized to ensure that analyses based on the column presents a truer representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b1207628",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equating similar entries in the funding stage column for ease of analysis\n",
    "complete_set[\"Funding Stage\"] = complete_set[\"Funding Stage\"].apply(lambda x: str(x).replace(\" Round\",\"\"))\n",
    "complete_set[\"Funding Stage\"] = complete_set[\"Funding Stage\"].apply(lambda x: str(x).replace(\" round\",\"\"))\n",
    "complete_set[\"Funding Stage\"] = complete_set[\"Funding Stage\"].apply(lambda x: str(x).replace(\" - Series Unknown\",\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7b64116c",
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# Equating similar entries in the Sector column for ease of analysis (2)\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"$6000000\", \"Funding Stage\"] = \"Venture\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Debt Financing\", \"Funding Stage\"] = \"Debt\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Early seed\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Fresh Funding\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Fresh funding\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"nan\", \"Funding Stage\"] = \"Undisclosed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"PE\", \"Funding Stage\"] = \"Private Equity\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre seed\", \"Funding Stage\"] = \"Pre-seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre series A1\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre- series A\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre-Series A\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre-Series A1\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed fund\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed funding\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed Funding\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seies A\", \"Funding Stage\"] = \"Series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Series A2\", \"Funding Stage\"] = \"Series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Series I\", \"Funding Stage\"] = \"Series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre Series A\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre series A\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre series B\", \"Funding Stage\"] = \"Pre-series B\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre series C\", \"Funding Stage\"] = \"Pre-series C\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre-Seed\", \"Funding Stage\"] = \"Pre-seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre-series A1\", \"Funding Stage\"] = \"Pre-series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed & Series A\", \"Funding Stage\"] = \"Series A\" # Checked Crunchbase\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed A\", \"Funding Stage\"] = \"Series A\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed Investment\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Seed+\", \"Funding Stage\"] = \"Seed\"\n",
    "complete_set.loc[complete_set[\"Funding Stage\"] == \"Pre-Series B\", \"Funding Stage\"] = \"Pre-series B\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03d5c175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seed                        604\n",
       "Series A                    305\n",
       "Pre-series A                209\n",
       "Series B                    134\n",
       "Series C                    114\n",
       "Seed Round                   69\n",
       "Pre series A                 62\n",
       "Pre-seed                     58\n",
       "Series D                     51\n",
       "Debt                         45\n",
       "Angel                        38\n",
       "Venture - Series Unknown     37\n",
       "Series E                     29\n",
       "Pre-series B                 14\n",
       "Debt Financing               13\n",
       "Name: Funding Stage, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a final preview of the revised column data\n",
    "unique_stages.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1d63bb9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Funding Deals per Funding Stage')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_stages = (complete_set.loc[:,\"Funding Stage\"]).value_counts()\n",
    "unique_stages.head(10).sort_values().plot.barh()\n",
    "plt.xlabel(\"Number of Funding Deals\")\n",
    "plt.ylabel(\"Funding Stage\")\n",
    "plt.title(\"Funding Deals per Funding Stage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1e0cf7",
   "metadata": {},
   "source": [
    "We note from the plot above that most of the funding stages for deals over the period were undisclosed. For the disclosed stages, \"Seed\" round had the most deals by a mile (924) followed by \"Series A\" (310) and \"Pre-Series A\" (289), with \"Series B\" and \"Series C\" following in the distance with 134 and 114 deals respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fadef331",
   "metadata": {},
   "source": [
    "#### 3.8.4 Amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b376f03d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            548\n",
       "1000000      169\n",
       "2000000      116\n",
       "3000000       81\n",
       "5000000       81\n",
       "            ... \n",
       "1065000        1\n",
       "11635000       1\n",
       "122000000      1\n",
       "110000         1\n",
       "6200000        1\n",
       "Name: Amount, Length: 512, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_amounts = (complete_set.loc[:,\"Amount\"]).value_counts()\n",
    "unique_amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7ab6ba5b",
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8f9b9089",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
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       "      <th>Investors</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>Satya microcapital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Microfinance company that offers collateral-fr...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1995</td>\n",
       "      <td>Vivek Tiwari</td>\n",
       "      <td>BlueOrchard Finance Limited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series C, D</td>\n",
       "      <td>42.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>E-commerce for corner/retail stores.</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra, Nidhi Ramachandran</td>\n",
       "      <td>CESC Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Ultraviolette automotive</td>\n",
       "      <td>Automobiles &amp; automotives</td>\n",
       "      <td>Series A</td>\n",
       "      <td>876.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Ultraviolette is a startup working on electric...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Next digital solutions</td>\n",
       "      <td>Advertising, marketing &amp; sales</td>\n",
       "      <td>Angel</td>\n",
       "      <td>1460.00</td>\n",
       "      <td>Kota</td>\n",
       "      <td>Next Digital Solutions is website design &amp; Dig...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Classworks india</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>1460.00</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Saas, Web App, Mobile App, School Education</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Swiggy</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Series H</td>\n",
       "      <td>1000000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Swiggy is a food ordering and delivery company...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>Reliance</td>\n",
       "      <td>Multinational conglomerate company</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance owns businesses across India engaged ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1973</td>\n",
       "      <td>Dhirubhai Ambani</td>\n",
       "      <td>Facebook, Google, KKR, Silver Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>Snowflake</td>\n",
       "      <td>Tech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>3000000000.00</td>\n",
       "      <td>California</td>\n",
       "      <td>Snowflake is a cloud data platform that provid...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2012</td>\n",
       "      <td>Benoit Dageville, Bob Muglia, Marcin Zukowski,...</td>\n",
       "      <td>Salesforce Ventures, Dragoneer Investment Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Reliance retail ventures ltd</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance Retail offers online shopping and a c...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mukesh Ambani</td>\n",
       "      <td>Silver Lake, Mubadala Investment Company</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>Alteria capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>150000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Alteria Capital is a Venture debt firm .</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Vinod Murali</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2307 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Company Name                              Sector  \\\n",
       "1162            Satya microcapital                             Fintech   \n",
       "1444                    Peel works                                Saas   \n",
       "70        Ultraviolette automotive           Automobiles & automotives   \n",
       "98          Next digital solutions      Advertising, marketing & sales   \n",
       "164               Classworks india                           Education   \n",
       "...                            ...                                 ...   \n",
       "101                         Swiggy                    Food & Nutrition   \n",
       "940                       Reliance  Multinational conglomerate company   \n",
       "929                      Snowflake                                Tech   \n",
       "892   Reliance retail ventures ltd                              Retail   \n",
       "1738               Alteria capital                             Fintech   \n",
       "\n",
       "     Funding Stage          Amount Headquarters  \\\n",
       "1162   Undisclosed            9.00        Delhi   \n",
       "1444   Series C, D           42.00       Mumbai   \n",
       "70        Series A          876.00    Bangalore   \n",
       "98           Angel         1460.00         Kota   \n",
       "164    Undisclosed         1460.00      Chennai   \n",
       "...            ...             ...          ...   \n",
       "101       Series H   1000000000.00    Bangalore   \n",
       "940    Undisclosed   2200000000.00       Mumbai   \n",
       "929    Undisclosed   3000000000.00   California   \n",
       "892    Undisclosed  70000000000.00       Mumbai   \n",
       "1738          Debt 150000000000.00       Mumbai   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "1162  Microfinance company that offers collateral-fr...         2020   \n",
       "1444               E-commerce for corner/retail stores.         2020   \n",
       "70    Ultraviolette is a startup working on electric...         2018   \n",
       "98    Next Digital Solutions is website design & Dig...         2018   \n",
       "164         Saas, Web App, Mobile App, School Education         2018   \n",
       "...                                                 ...          ...   \n",
       "101   Swiggy is a food ordering and delivery company...         2018   \n",
       "940   Reliance owns businesses across India engaged ...         2020   \n",
       "929   Snowflake is a cloud data platform that provid...         2020   \n",
       "892   Reliance Retail offers online shopping and a c...         2020   \n",
       "1738           Alteria Capital is a Venture debt firm .         2021   \n",
       "\n",
       "     Year Founded                                           Founders  \\\n",
       "1162         1995                                       Vivek Tiwari   \n",
       "1444         2010                 Sachin Chhabra, Nidhi Ramachandran   \n",
       "70            nan                                                NaN   \n",
       "98            nan                                                NaN   \n",
       "164           nan                                                NaN   \n",
       "...           ...                                                ...   \n",
       "101           nan                                                NaN   \n",
       "940          1973                                   Dhirubhai Ambani   \n",
       "929          2012  Benoit Dageville, Bob Muglia, Marcin Zukowski,...   \n",
       "892          2006                                      Mukesh Ambani   \n",
       "1738       2018.0                                       Vinod Murali   \n",
       "\n",
       "                                            Investors  \n",
       "1162                      BlueOrchard Finance Limited  \n",
       "1444                                    CESC Ventures  \n",
       "70                                                NaN  \n",
       "98                                                NaN  \n",
       "164                                               NaN  \n",
       "...                                               ...  \n",
       "101                                               NaN  \n",
       "940                Facebook, Google, KKR, Silver Lake  \n",
       "929   Salesforce Ventures, Dragoneer Investment Group  \n",
       "892          Silver Lake, Mubadala Investment Company  \n",
       "1738                                              NaN  \n",
       "\n",
       "[2307 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing all Nulls with 0\n",
    "complete_set.loc[complete_set[\"Amount\"] == \"nan\", \"Amount\"] = np.nan\n",
    "complete_set[\"Amount\"].fillna(0.00,inplace = True)\n",
    "\n",
    "# Creating a copy of the DataFrame sorted by Amount\n",
    "amt_sorting = (complete_set.loc[complete_set[\"Amount\"] != 0]).round(2).sort_values(by = \"Amount\")\n",
    "amt_sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c008946f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 470 \n",
      "\n",
      "Outliers percentage of total: 16.46234676007005% \n",
      "\n",
      "Max outlier value: 150000000000.0 \n",
      "\n",
      "Min outlier value: 20000000.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Creating a function to find outliers using IQR\n",
    "def outliers_IQR(df):\n",
    "    q1 = df.quantile(0.25)\n",
    "    q3 = df.quantile(0.75)\n",
    "    IQR = q3-q1\n",
    "    outliers = df[((df<(q1-1.5*IQR)) | (df>(q3+1.5*IQR)))]\n",
    "    return outliers\n",
    "\n",
    "outliers = outliers_IQR(complete_set[\"Amount\"])\n",
    "print(\"Number of outliers: \" + str(len(outliers)), \"\\n\")\n",
    "print(\"Outliers percentage of total: \" + str((((len(outliers))/len(complete_set[\"Amount\"])*100))) + \"%\", \"\\n\")\n",
    "print(\"Max outlier value: \" + str(outliers.max()), \"\\n\")\n",
    "print(\"Min outlier value: \" + str(outliers.min()), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d173f2",
   "metadata": {},
   "source": [
    "Since the outliers represent over 16% of the data available, it would be unwise to remove all of them. Outliers from the top and bottom will be selected for confirmation online, then further action taken on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5e9e5319",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Company Name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>Satya microcapital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Microfinance company that offers collateral-fr...</td>\n",
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       "      <td>1995</td>\n",
       "      <td>Vivek Tiwari</td>\n",
       "      <td>BlueOrchard Finance Limited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series C, D</td>\n",
       "      <td>42.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>E-commerce for corner/retail stores.</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra, Nidhi Ramachandran</td>\n",
       "      <td>CESC Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Ultraviolette automotive</td>\n",
       "      <td>Automobiles &amp; automotives</td>\n",
       "      <td>Series A</td>\n",
       "      <td>876.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Ultraviolette is a startup working on electric...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Next digital solutions</td>\n",
       "      <td>Advertising, marketing &amp; sales</td>\n",
       "      <td>Angel</td>\n",
       "      <td>1460.00</td>\n",
       "      <td>Kota</td>\n",
       "      <td>Next Digital Solutions is website design &amp; Dig...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Classworks india</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>1460.00</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Saas, Web App, Mobile App, School Education</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Enlyft digital solutions private limited</td>\n",
       "      <td>—</td>\n",
       "      <td>Seed</td>\n",
       "      <td>2920.00</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Digital Marketing Agency</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Antariksh waste ventures pvt ltd</td>\n",
       "      <td>IoT</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Waste Management for Smart Cities</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Mombay</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Seed</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Mombay is a unique opportunity for housewives ...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Authmetrik</td>\n",
       "      <td>B2b</td>\n",
       "      <td>Grant</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>SaaS, B2B, Security, Stop account sharing, Fra...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>Glii</td>\n",
       "      <td>Social media &amp; networking</td>\n",
       "      <td>Seed</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>India’s finest dating companionship application</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Shivam Kaushik</td>\n",
       "      <td>We Founder Circle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Company Name  \\\n",
       "1162                        Satya microcapital   \n",
       "1444                                Peel works   \n",
       "70                    Ultraviolette automotive   \n",
       "98                      Next digital solutions   \n",
       "164                           Classworks india   \n",
       "258   Enlyft digital solutions private limited   \n",
       "19            Antariksh waste ventures pvt ltd   \n",
       "522                                     Mombay   \n",
       "95                                  Authmetrik   \n",
       "1954                                      Glii   \n",
       "\n",
       "                              Sector Funding Stage   Amount Headquarters  \\\n",
       "1162                         Fintech   Undisclosed     9.00        Delhi   \n",
       "1444                            Saas   Series C, D    42.00       Mumbai   \n",
       "70         Automobiles & automotives      Series A   876.00    Bangalore   \n",
       "98    Advertising, marketing & sales         Angel  1460.00         Kota   \n",
       "164                        Education   Undisclosed  1460.00      Chennai   \n",
       "258                                —          Seed  2920.00         Pune   \n",
       "19                               IoT          Seed  7300.00      Chennai   \n",
       "522                 Food & Nutrition          Seed  7500.00       Mumbai   \n",
       "95                               B2b         Grant 10000.00      Gurgaon   \n",
       "1954       Social media & networking          Seed 10000.00        Noida   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "1162  Microfinance company that offers collateral-fr...         2020   \n",
       "1444               E-commerce for corner/retail stores.         2020   \n",
       "70    Ultraviolette is a startup working on electric...         2018   \n",
       "98    Next Digital Solutions is website design & Dig...         2018   \n",
       "164         Saas, Web App, Mobile App, School Education         2018   \n",
       "258                            Digital Marketing Agency         2018   \n",
       "19                    Waste Management for Smart Cities         2018   \n",
       "522   Mombay is a unique opportunity for housewives ...         2018   \n",
       "95    SaaS, B2B, Security, Stop account sharing, Fra...         2018   \n",
       "1954    India’s finest dating companionship application         2021   \n",
       "\n",
       "     Year Founded                            Founders  \\\n",
       "1162         1995                        Vivek Tiwari   \n",
       "1444         2010  Sachin Chhabra, Nidhi Ramachandran   \n",
       "70            nan                                 NaN   \n",
       "98            nan                                 NaN   \n",
       "164           nan                                 NaN   \n",
       "258           nan                                 NaN   \n",
       "19            nan                                 NaN   \n",
       "522           nan                                 NaN   \n",
       "95            nan                                 NaN   \n",
       "1954       2020.0                      Shivam Kaushik   \n",
       "\n",
       "                        Investors  \n",
       "1162  BlueOrchard Finance Limited  \n",
       "1444                CESC Ventures  \n",
       "70                            NaN  \n",
       "98                            NaN  \n",
       "164                           NaN  \n",
       "258                           NaN  \n",
       "19                            NaN  \n",
       "522                           NaN  \n",
       "95                            NaN  \n",
       "1954            We Founder Circle  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Who could be the outliers?\n",
    "## Looking at the outliers from the top\n",
    "(amt_sorting.loc[amt_sorting[\"Amount\"] > 0]).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "92040666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2840000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra, Nidhi Ramachandran</td>\n",
       "      <td>CESC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2855</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra, Nidhi Ramachandran</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Debt</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra, Nidhi Ramachandran</td>\n",
       "      <td>BlackSoil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Debt</td>\n",
       "      <td>408000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra, Nidhi Ramachandran</td>\n",
       "      <td>Equanimity Investments</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company Name Sector Funding Stage     Amount Headquarters Description  \\\n",
       "2854   Peel works   Saas      Series D 2840000.00       Mumbai         NaN   \n",
       "2855   Peel works   Saas      Series D 2000000.00       Mumbai         NaN   \n",
       "2856   Peel works   Saas          Debt 1000000.00       Mumbai         NaN   \n",
       "2857   Peel works   Saas          Debt  408000.00       Mumbai         NaN   \n",
       "\n",
       "     Funding Year Year Founded                            Founders  \\\n",
       "2854         2020         2010  Sachin Chhabra, Nidhi Ramachandran   \n",
       "2855         2020         2010  Sachin Chhabra, Nidhi Ramachandran   \n",
       "2856         2020         2010  Sachin Chhabra, Nidhi Ramachandran   \n",
       "2857         2020         2010  Sachin Chhabra, Nidhi Ramachandran   \n",
       "\n",
       "                   Investors  \n",
       "2854                    CESC  \n",
       "2855                     NaN  \n",
       "2856               BlackSoil  \n",
       "2857  Equanimity Investments  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correcting the erroneous entries\n",
    "## SATYA Microcapital (raised ₹725M debt financing from BlueOrchard in 2020 to be converted at 74.1322 INR/USD)\n",
    "complete_set.loc[(complete_set[\"Company Name\"] == \"Satya microcapital\") & \n",
    "                 (complete_set[\"Amount\"] == 9.00), \n",
    "                 [\"Funding Stage\", \"Amount\", \"Investors\"]] = [\"Debt\", int(725000000/74.1322), \"BlueOrchard Finance S A\"]\n",
    "\n",
    "## Ultraviolette Automotive: raised ₹60M Series A funding in 2018 \n",
    "complete_set.loc[(complete_set[\"Company Name\"] == \"Ultraviolette automotive\") &\n",
    "                 (complete_set[\"Amount\"] == 876.00), \n",
    "                 [\"Funding Stage\", \"Amount\"]] = [\"Series A\", int(60000000*0.0146)]\n",
    "\n",
    "## Peel Works received lots of funding in 2020 not available in the dataset, I will therefore add them:\n",
    "peel_addon = pd.DataFrame({'Company Name': [\"Peel works\",\"Peel works\",\"Peel works\",\"Peel works\"], \n",
    "              'Sector': [\"Saas\", \"Saas\", \"Saas\",\" Saas\"], \n",
    "              'Funding Stage': [\"Series D\",\"Series D\",\"Debt\",\"Debt\"],\n",
    "              'Amount': [2840000, 2000000, 1000000, 408000], \n",
    "              'Headquarters': [\"Mumbai\",\"Mumbai\",\"Mumbai\",\"Mumbai\"],\n",
    "              'Description': [np.nan, np.nan, np.nan, np.nan], \n",
    "              'Funding Year': [\"2020\", \"2020\", \"2020\", \"2020\"],\n",
    "              'Year Founded': [\"2010\", \"2010\", \"2010\", \"2010\"], \n",
    "              'Founders': [\"Sachin Chhabra, Nidhi Ramachandran\",\"Sachin Chhabra, Nidhi Ramachandran\",\"Sachin Chhabra, Nidhi Ramachandran\",\"Sachin Chhabra, Nidhi Ramachandran\"],\n",
    "              'Investors': [\"CESC\", np.nan, \"BlackSoil\", \"Equanimity Investments\"]})\n",
    "\n",
    "# Dropping the erroneous column and inserting the corrections\n",
    "complete_set = complete_set.drop([1444], axis = 0) \n",
    "complete_set = pd.concat([complete_set,peel_addon], ignore_index=True, axis = 0)\n",
    "\n",
    "complete_set.loc[(complete_set[\"Company Name\"] == \"Peel works\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c092426c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Sbi life</td>\n",
       "      <td>Insurance</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>742000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>SBI Life is one of the life insurance company ...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>Swiggy</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>800000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Swiggy is an on-demand food delivery platform ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Nandan Reddy, Rahul Jaimini, Sriharsha Majety</td>\n",
       "      <td>Carmignac, Falcon Edge Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>Reliance retail ventures</td>\n",
       "      <td>E-commerce</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>800000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance Retail offers online shopping and a c...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mukesh Ambani</td>\n",
       "      <td>General Atlantic, Kohlberg Kravis Roberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>Dream sports</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>840000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Dream Sports is India’s leading sports technol...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Harsh Jain</td>\n",
       "      <td>Falcon Edge, DST Global, D1 Capital, Redbird C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>Verse innovation</td>\n",
       "      <td>Innovation management</td>\n",
       "      <td>Series H</td>\n",
       "      <td>1000000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Owner of Dailyhunt App and I-Pay</td>\n",
       "      <td>2021</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Virendra Gupta</td>\n",
       "      <td>Canaan Valley Capital, Glade Brook Capital Par...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Swiggy</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Series H</td>\n",
       "      <td>1000000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Swiggy is a food ordering and delivery company...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>Reliance</td>\n",
       "      <td>Multinational conglomerate company</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance owns businesses across India engaged ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1973</td>\n",
       "      <td>Dhirubhai Ambani</td>\n",
       "      <td>Facebook, Google, KKR, Silver Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>Snowflake</td>\n",
       "      <td>Tech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>3000000000.00</td>\n",
       "      <td>California</td>\n",
       "      <td>Snowflake is a cloud data platform that provid...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2012</td>\n",
       "      <td>Benoit Dageville, Bob Muglia, Marcin Zukowski,...</td>\n",
       "      <td>Salesforce Ventures, Dragoneer Investment Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Reliance retail ventures ltd</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance Retail offers online shopping and a c...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mukesh Ambani</td>\n",
       "      <td>Silver Lake, Mubadala Investment Company</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>Alteria capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>150000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Alteria Capital is a Venture debt firm .</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Vinod Murali</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Company Name                              Sector  \\\n",
       "90                        Sbi life                           Insurance   \n",
       "1718                        Swiggy                    Food & Nutrition   \n",
       "914       Reliance retail ventures                          E-commerce   \n",
       "2581                  Dream sports                              Sports   \n",
       "1992              Verse innovation               Innovation management   \n",
       "101                         Swiggy                    Food & Nutrition   \n",
       "940                       Reliance  Multinational conglomerate company   \n",
       "929                      Snowflake                                Tech   \n",
       "892   Reliance retail ventures ltd                              Retail   \n",
       "1738               Alteria capital                             Fintech   \n",
       "\n",
       "       Funding Stage          Amount Headquarters  \\\n",
       "90    Private Equity    742000000.00       Mumbai   \n",
       "1718     Undisclosed    800000000.00    Bangalore   \n",
       "914      Undisclosed    800000000.00       Mumbai   \n",
       "2581     Undisclosed    840000000.00       Mumbai   \n",
       "1992        Series H   1000000000.00    Bangalore   \n",
       "101         Series H   1000000000.00    Bangalore   \n",
       "940      Undisclosed   2200000000.00       Mumbai   \n",
       "929      Undisclosed   3000000000.00   California   \n",
       "892      Undisclosed  70000000000.00       Mumbai   \n",
       "1738            Debt 150000000000.00       Mumbai   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "90    SBI Life is one of the life insurance company ...         2018   \n",
       "1718  Swiggy is an on-demand food delivery platform ...         2021   \n",
       "914   Reliance Retail offers online shopping and a c...         2020   \n",
       "2581  Dream Sports is India’s leading sports technol...         2021   \n",
       "1992                   Owner of Dailyhunt App and I-Pay         2021   \n",
       "101   Swiggy is a food ordering and delivery company...         2018   \n",
       "940   Reliance owns businesses across India engaged ...         2020   \n",
       "929   Snowflake is a cloud data platform that provid...         2020   \n",
       "892   Reliance Retail offers online shopping and a c...         2020   \n",
       "1738           Alteria Capital is a Venture debt firm .         2021   \n",
       "\n",
       "     Year Founded                                           Founders  \\\n",
       "90            nan                                                NaN   \n",
       "1718       2014.0      Nandan Reddy, Rahul Jaimini, Sriharsha Majety   \n",
       "914          2006                                      Mukesh Ambani   \n",
       "2581       2008.0                                         Harsh Jain   \n",
       "1992       2007.0                                     Virendra Gupta   \n",
       "101           nan                                                NaN   \n",
       "940          1973                                   Dhirubhai Ambani   \n",
       "929          2012  Benoit Dageville, Bob Muglia, Marcin Zukowski,...   \n",
       "892          2006                                      Mukesh Ambani   \n",
       "1738       2018.0                                       Vinod Murali   \n",
       "\n",
       "                                              Investors  \n",
       "90                                                  NaN  \n",
       "1718                     Carmignac, Falcon Edge Capital  \n",
       "914           General Atlantic, Kohlberg Kravis Roberts  \n",
       "2581  Falcon Edge, DST Global, D1 Capital, Redbird C...  \n",
       "1992  Canaan Valley Capital, Glade Brook Capital Par...  \n",
       "101                                                 NaN  \n",
       "940                  Facebook, Google, KKR, Silver Lake  \n",
       "929     Salesforce Ventures, Dragoneer Investment Group  \n",
       "892            Silver Lake, Mubadala Investment Company  \n",
       "1738                                                NaN  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Looking at the outliers from the bottom\n",
    "(amt_sorting.loc[amt_sorting[\"Amount\"] > 0]).tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "715f4194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Funding Amounts')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rgUOSHNwc6DweuHhGm9uAVwIk+RXgucAtgyxUkjS/vXo1qKptSd4OXAGMAGurakOSU5vt5wEfBC5Icj2dKZozququnVi3JGmGnoEOUFWXAZfNWHde1/VNwG8OtjRJ0kL4SVFJagkDXZJawkCXpJYw0CWpJQx0SWoJA13qMjk5ydjYGCMjI4yNjTE5OTnskqS+9XXaovR4MDk5yerVqzn//PM54ogjWL9+PRMTEwCsXLlyyNVJvaVq5tey7Brj4+M1NTU1lH1LsxkbG+Pcc8/lqKOOemjdunXrWLVqFTfccMMQK5MeluSaqhqfdZuBLnWMjIxw3333sffeez+0buvWrey777488MADQ6xMeth8ge4cutRYvnw569evf8S69evXs3z58iFVJC2MgS41Vq9ezcTEBOvWrWPr1q2sW7eOiYkJVq9ePezSpL54UFRqbD/wuWrVKm688UaWL1/OWWed5QFR7TGcQ5ekPYhz6JL0OGCgS1JLGOiS1BIGuiS1hIEuSS3RV6AnOTrJTUk2JjlzjjZHJrk2yYYkfz/YMiVJvfQ8Dz3JCPAx4N8D08DVSS6uqn/qarM/8HHg6Kq6Lckv76R6JUlz6GeEfjiwsapuqapfABcBx85o8ybgS1V1G0BV3TnYMiVJvfQT6EuA27uWp5t13Z4DPDXJN5Jck+TNs3WU5JQkU0mmNm/evGMVS5Jm1U+gZ5Z1Mz9euhfwIuC3gFcD70vynEf9UdWaqhqvqvHR0dEFFytJmls/3+UyDRzYtXwAsGmWNndV1RZgS5IrgcOAmwdSpSSpp35G6FcDhyQ5OMk+wPHAxTPafAV4WZK9kjwReAlw42BLlSTNp+cIvaq2JXk7cAUwAqytqg1JTm22n1dVNyb5KnAd8CDwiaryJ14kaRfy2xYlaQ/ity1K0uOAgS5JLWGgS1JLGOiS1BIGuiS1hIEuSS1hoEtSSxjoktQSBroktYSBLkktYaBLUksY6JLUEga6JLWEgS5JLWGgS1JLGOiS1BIGuiS1RF+BnuToJDcl2ZjkzHnavTjJA0mOG1yJkqR+9Az0JCPAx4BjgBXAyiQr5mh3Np3fHpUk7WL9jNAPBzZW1S1V9QvgIuDYWdqtAr4I3DnA+iRJfeon0JcAt3ctTzfrHpJkCfB64Lz5OkpySpKpJFObN29eaK2SpHn0E+iZZV3NWP4IcEZVPTBfR1W1pqrGq2p8dHS0zxIlSf3Yq48208CBXcsHAJtmtBkHLkoCsAh4TZJtVfXlQRQpSeqtn0C/GjgkycHAD4DjgTd1N6iqg7dfT3IBcKlhLkm7Vs9Ar6ptSd5O5+yVEWBtVW1Icmqzfd55c0nSrtHPCJ2qugy4bMa6WYO8qt7y2MuSJC2UnxSVpJYw0CWpJQx0SWoJA12SWsJAl6SWMNAlqSUMdElqCQNdklrCQJekljDQJaklDHRJagkDXZJawkCXpJYw0CWpJQx0SWoJA12SWsJAl6SW6CvQkxyd5KYkG5OcOcv2E5Jc11yuSnLY4EuVJM2nZ6AnGQE+BhwDrABWJlkxo9mtwMur6lDgg8CaQRcqSZpfPyP0w4GNVXVLVf0CuAg4trtBVV1VVf/SLH4LOGCwZUqSeukn0JcAt3ctTzfr5jIBXP5YipIkLdxefbTJLOtq1obJUXQC/Yg5tp8CnAKwdOnSPkuUJPWjnxH6NHBg1/IBwKaZjZIcCnwCOLaqfjxbR1W1pqrGq2p8dHR0R+qVJM2hn0C/GjgkycFJ9gGOBy7ubpBkKfAl4MSqunnwZUqSeuk55VJV25K8HbgCGAHWVtWGJKc2288D3g88Hfh4EoBtVTW+88qWJM2Uqlmnw3e68fHxmpqaGsq+JWlPleSauQbMflJUklrCQJekljDQJaklDHRJagkDXZJawkCXpJYw0CWpJQx0SWoJA12SWsJAl6SWMNAlqSUMdElqCQNdklrCQJekljDQpS6Tk5OMjY0xMjLC2NgYk5OTwy5J6puBLjUmJyc57bTT2LJlC1XFli1bOO200wx17TEMdKlx+umnMzIywtq1a7n//vtZu3YtIyMjnH766cMuTepLz5+gkx4vpqenAXjFK14x5EqkHdPXCD3J0UluSrIxyZmzbE+Sc5rt1yV54eBLlSTNp2egJxkBPgYcA6wAViZZMaPZMcAhzeUU4C8HXKckqYd+RuiHAxur6paq+gVwEXDsjDbHAp+sjm8B+yd5xoBrlXaJZcuW8YQnPIFly5YNuxRpQfqZQ18C3N61PA28pI82S4A7uhslOYXOCJ6lS5cutFaJ5//183da32MXjD10fQWdN6FjjO3U/V5/0vU7pV89PvUT6JllXe1AG6pqDbAGYHx8/FHbpV52ZgAmDz+Nzz77bM4444yHlqt8umr318+UyzRwYNfyAcCmHWgj7TG6w1zaU/QT6FcDhyQ5OMk+wPHAxTPaXAy8uTnb5aXAT6rqjpkdSbuzuUbhjs61p+g55VJV25K8HbgCGAHWVtWGJKc2288DLgNeA2wEfgacvPNKlnYew1t7sr4+WFRVl9EJ7e5153VdL+Btgy1NkrQQfvRfklrCQJekljDQJaklDHRJaokM66h+ks3A94eyc6m3RcBdwy5CmsVBVTU624ahBbq0O0syVVXjw65DWginXCSpJQx0SWoJA12a3ZphFyAtlHPoktQSjtAlqSUMdElqCQNdrZLk9UkqyfOGWMM7kzxxWPvX45eBrrZZCayn8739w/JOwEDXLmegqzWS7Af8BjBBE+hJjkzy90k+l+TmJB9KckKSbye5PsmzmnYHJfl6kuuaf5c26y9IclzXPu7t6vcbSb6Q5LtJPt38wMs7gMXAuiTrdvFdoMc5A11t8h+Br1bVzcDdSV7YrD8MOA14PnAi8JyqOhz4BLCqafNR4JNVdSjwaeCcPvb3a3RG4yuAZwK/UVXn0Pn5xaOq6qhB3CipXwa62mQlcFFz/aJmGeDqqrqjqu4H/hn4WrP+emBZc/3Xgc801y8Ejuhjf9+uqumqehC4tqsvaSj6+sUiaXeX5OnAK4CxJEXn5xKLzi9t3d/V9MGu5QeZ+//A9g9obKMZ+CQJsE9Xm+5+H5inL2mXcISutjiOzpTJQVW1rKoOBG6lv5E2wFU8fCD1BDoHVgG+B7youX4ssHcffd0DPKnP/UoDY6CrLVYCfzNj3ReBN/X59+8ATk5yHZ159tOa9f8LeHmSbwMvAbb00dca4HIPimpX86P/ktQSjtAlqSUMdElqCQNdklrCQJekljDQJaklDHRJagkDXZJa4v8DEzINTBYwaDUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to summarize the amounts\n",
    "(amt_sorting.loc[amt_sorting[\"Amount\"] > 0]).plot.box()\n",
    "(amt_sorting.loc[amt_sorting[\"Amount\"] > 0]).describe()\n",
    "plt.title(\"Distribution of Funding Amounts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "67c91010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Reliance retail ventures ltd</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance Retail offers online shopping and a c...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mukesh Ambani</td>\n",
       "      <td>Silver Lake, Mubadala Investment Company</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>Alteria capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>150000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Alteria Capital is a Venture debt firm .</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Vinod Murali</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Company Name   Sector Funding Stage          Amount  \\\n",
       "892   Reliance retail ventures ltd   Retail   Undisclosed  70000000000.00   \n",
       "1737               Alteria capital  Fintech          Debt 150000000000.00   \n",
       "\n",
       "     Headquarters                                        Description  \\\n",
       "892        Mumbai  Reliance Retail offers online shopping and a c...   \n",
       "1737       Mumbai           Alteria Capital is a Venture debt firm .   \n",
       "\n",
       "     Funding Year Year Founded       Founders  \\\n",
       "892          2020         2006  Mukesh Ambani   \n",
       "1737         2021       2018.0   Vinod Murali   \n",
       "\n",
       "                                     Investors  \n",
       "892   Silver Lake, Mubadala Investment Company  \n",
       "1737                                       NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set.loc[(complete_set[\"Amount\"] == 70000000000.00) |\n",
    "                (complete_set[\"Amount\"] == 150000000000.00)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f814447",
   "metadata": {},
   "source": [
    "Despite being correct entries, the top 2 outliers Alteria Capital (index 1737) and Reliance Retail Ventures Ltd (index 892) will be dropped to assess their impact on the amounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "48d9a5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_set_no_outliers = complete_set.drop([892, 1737], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4c4a4fca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2308.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25900134.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>107456008.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1460.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3000000000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Amount\n",
       "count       2308.00\n",
       "mean    25900134.96\n",
       "std    107456008.88\n",
       "min         1460.00\n",
       "25%      1000000.00\n",
       "50%      3000000.00\n",
       "75%     12000000.00\n",
       "max   3000000000.00"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Re-assigning the sorted DataFrame and displaying the revised DataFrame in a boxplot\n",
    "amt_sorting = (complete_set_no_outliers.loc[complete_set_no_outliers[\"Amount\"] > 0]).round(2).sort_values(by = \"Amount\")\n",
    "(amt_sorting.loc[amt_sorting[\"Amount\"] > 0]).plot.box()\n",
    "(amt_sorting.loc[amt_sorting[\"Amount\"] > 0]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b39096",
   "metadata": {},
   "source": [
    "Per the boxplots for the complete set with the two major outliers and that without the two major outliers, their effects on the mean is evident, at USD 121.27m with outliers and USD 25.90m without outliers. This represents a stark USD 95.37m difference between the two datasets. Even so, it is visible from the boxplot for the complete set without outliers that there are two more big outliers dragging the USD 25.90m mean outward, with the next looking relatively close to the norm.\n",
    "\n",
    "As such, the **median** of **USD 3m** (in both cases - with and without outliers) will be used for computations and analysis for the \"average\" startup. That is to say that the average funding deal over the period was worth **USD 3m**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70e6675",
   "metadata": {},
   "source": [
    "#### 3.8.5 Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "45ad37ff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bangalore      859\n",
       "Mumbai         471\n",
       "Gurugram       237\n",
       "New Delhi      230\n",
       "Chennai        106\n",
       "              ... \n",
       "France           1\n",
       "Palmwoods        1\n",
       "Milano           1\n",
       "Bangaldesh       1\n",
       "Gandhinagar      1\n",
       "Name: Headquarters, Length: 134, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_locations = (complete_set.loc[:,\"Headquarters\"]).value_counts()\n",
    "unique_locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "197182d9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bangalore    916\n",
       "Mumbai       471\n",
       "Gurgaon      317\n",
       "New Delhi    230\n",
       "Chennai      106\n",
       "Pune         104\n",
       "Unknown       94\n",
       "Delhi         88\n",
       "Noida         86\n",
       "Hyderabad     76\n",
       "Name: Headquarters, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set[\"Headquarters\"].apply(str)\n",
    "complete_set[\"Headquarters\"].fillna(\"Unknown Location\", inplace = True)\n",
    "complete_set.loc[complete_set[\"Headquarters\"] == \"nan\", \"Headquarters\"] = \"Unknown\"\n",
    "complete_set.loc[complete_set[\"Headquarters\"] == \"Bengaluru\", \"Headquarters\"] = \"Bangalore\"\n",
    "complete_set.loc[complete_set[\"Headquarters\"] == \"Bombay\", \"Headquarters\"] = \"Mumbai\"\n",
    "complete_set.loc[complete_set[\"Headquarters\"] == \"Gurugram\", \"Headquarters\"] = \"Gurgaon\"\n",
    "complete_set.loc[complete_set[\"Headquarters\"] == \"Mountain View, CA\", \"Headquarters\"] = \"California\"\n",
    "\n",
    "unique_locations = (complete_set.loc[:,\"Headquarters\"]).value_counts()\n",
    "unique_locations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "da6bce25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Funding Deals per City')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_locations.head(10).sort_values().plot.barh()\n",
    "plt.xlabel(\"Number of Funding Deals\")\n",
    "plt.ylabel(\"City\")\n",
    "plt.title(\"Funding Deals per City\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baaae779",
   "metadata": {},
   "source": [
    "From the above, it is seen that Bangalore (916) leads the pack (as the city with most startups involved in deals) by almost twice as much as the next location, Mumbai, which has 471. It also leaves a fair distance between itself and Gurgaon (317), New Delhi (230), Chennai (106), and Pune (104) in that order.\n",
    "\n",
    "These make up about 75% of the total transactions over the period, thus pointing to part acceptance of the null hypothesis which posits that funding to startups is centralized around specific locations and sectors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a99bcb",
   "metadata": {},
   "source": [
    "#### 3.8.6 Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c3209d4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BYJU'S is an educational technology company that develops personalized learning programs for K-12 students.    5\n",
       "Provides online learning classes                                                                               5\n",
       "Onine learning platform                                                                                        4\n",
       "Mobile Premier League is a skill-based eSports platform used to offer cash prizes while playing games.         4\n",
       "BharatPe develops a QR code-based payment app for offline retailers and businesses.                            4\n",
       "                                                                                                              ..\n",
       "Known for its larger brands – Faasos, Behrouz Biryani, Oven story and Mandarin                                 1\n",
       "Cosmetics & beauty products online                                                                             1\n",
       "Crowdfunding, Charity, E-commerce                                                                              1\n",
       "Banks that extends two distinct fixed deposit schemes                                                          1\n",
       "It enables businesses to execute, track, predict and oprimize the movements of goods                           1\n",
       "Name: Description, Length: 2690, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set[\"Description\"].apply(str) # Applying a string formatting to the column\n",
    "complete_set[\"Description\"].fillna(\"Unknown\", inplace = True) # Filling null spaces with Unknown\n",
    "complete_set[\"Description\"].replace(\"  \",\" \", inplace = True) # Replacing all double spaces\n",
    "company_descs = (complete_set.loc[:,\"Description\"]).value_counts()\n",
    "company_descs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb247e02",
   "metadata": {},
   "source": [
    "#### 3.8.7 Funding Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3145e529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018     525\n",
       "2019      89\n",
       "2020    1054\n",
       "2021    1190\n",
       "Name: Funding Year, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set[\"Funding Year\"].apply(str) # Applying string formatting\n",
    "complete_set[\"Funding Year\"].fillna(np.nan, inplace = True) # Filling unknown spaces with null\n",
    "funding_year = (complete_set.loc[:,\"Funding Year\"]).value_counts()\n",
    "funding_year = funding_year.sort_index()\n",
    "funding_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d04c6328",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Deals')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "funding_year.plot()\n",
    "plt.title(\"Number of Deals per Year\")\n",
    "plt.ylabel(\"Year of Funding\")\n",
    "plt.xlabel(\"Number of Deals\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7355b379",
   "metadata": {},
   "source": [
    "Here, we note that the number of funding deals, despite the big drop in 2019, is increasing. Number of funding deals increased from 525 in 2018 to 1190 in 2021. This implies that chances are fairly high that a new startup would be able to land a funding deal going forward since the number of deals is on an increasing trajectory.\n",
    "\n",
    "What about the amounts? How have the total amounts invested changed over the period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c1f38840",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Funding Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>525</td>\n",
       "      <td>6642398301.00</td>\n",
       "      <td>12652187.24</td>\n",
       "      <td>584000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>89</td>\n",
       "      <td>3336433200.00</td>\n",
       "      <td>37488013.48</td>\n",
       "      <td>5500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1054</td>\n",
       "      <td>90187104429.00</td>\n",
       "      <td>85566512.74</td>\n",
       "      <td>1440000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1190</td>\n",
       "      <td>179611575552.00</td>\n",
       "      <td>150934097.10</td>\n",
       "      <td>2500000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count             sum         mean     median\n",
       "Funding Year                                               \n",
       "2018            525   6642398301.00  12652187.24  584000.00\n",
       "2019             89   3336433200.00  37488013.48 5500000.00\n",
       "2020           1054  90187104429.00  85566512.74 1440000.00\n",
       "2021           1190 179611575552.00 150934097.10 2500000.00"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at how much the average startup raised based on their founding year\n",
    "funding_set = complete_set.groupby(\"Funding Year\").Amount.agg([\"count\",\"sum\", \"mean\", \"median\"])\n",
    "funding_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d9253f25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Year of Funding')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = funding_set.index, y = funding_set[\"sum\"])\n",
    "plt.title(\"Total Amount of Funding per Year\")\n",
    "plt.ylabel(\"Total Amount of Funding\")\n",
    "plt.xlabel(\"Year of Funding\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935dc9fd",
   "metadata": {},
   "source": [
    "We note that in line with the increasing number of deals over the period, total amounts invested have been increasing over the period, with investments in 2021 having the highest monetary value.\n",
    "\n",
    "We must not ignore the fact that despite 2018 having a higher number of transactions, the average amount per deal (USD 12.7bn) was significantly less than that of 2019 (USD 37.5bn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3281361e",
   "metadata": {},
   "source": [
    "#### 3.8.8 Year Founded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "98751f04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020.0    231\n",
       "2019.0    180\n",
       "2018.0    148\n",
       "2015.0    140\n",
       "2015      135\n",
       "2016.0    125\n",
       "2018      123\n",
       "2017.0    117\n",
       "2019      104\n",
       "2016      103\n",
       "2017       92\n",
       "2014       71\n",
       "2021.0     70\n",
       "2014.0     64\n",
       "2020       42\n",
       "2012       39\n",
       "2011.0     37\n",
       "2013       36\n",
       "2012.0     33\n",
       "2011       30\n",
       "2013.0     28\n",
       "2010       19\n",
       "2010.0     18\n",
       "2008       18\n",
       "2008.0     16\n",
       "2009.0      7\n",
       "2009        7\n",
       "2007.0      6\n",
       "2000.0      6\n",
       "2006        6\n",
       "2007        4\n",
       "2004.0      3\n",
       "2006.0      3\n",
       "1994.0      2\n",
       "1998.0      2\n",
       "1995        2\n",
       "1999.0      2\n",
       "1989.0      2\n",
       "2002.0      2\n",
       "1999        1\n",
       "2005.0      1\n",
       "1984.0      1\n",
       "1991.0      1\n",
       "2003.0      1\n",
       "1978.0      1\n",
       "1998        1\n",
       "1978        1\n",
       "1994        1\n",
       "1993.0      1\n",
       "1982        1\n",
       "2001        1\n",
       "2004        1\n",
       "2002        1\n",
       "-           1\n",
       "1973        1\n",
       "2005        1\n",
       "1963.0      1\n",
       "Name: Year Founded, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((complete_set.loc[:,\"Year Founded\"]).value_counts())[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9d1d2ac8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1963      1\n",
       "1973      1\n",
       "1978      2\n",
       "1982      1\n",
       "1984      1\n",
       "1989      2\n",
       "1991      1\n",
       "1993      1\n",
       "1994      3\n",
       "1995      2\n",
       "1998      3\n",
       "1999      3\n",
       "2000      6\n",
       "2001      1\n",
       "2002      3\n",
       "2003      1\n",
       "2004      4\n",
       "2005      2\n",
       "2006      9\n",
       "2007     10\n",
       "2008     34\n",
       "2009     14\n",
       "2010     37\n",
       "2011     67\n",
       "2012     72\n",
       "2013     64\n",
       "2014    135\n",
       "2015    275\n",
       "2016    228\n",
       "2017    209\n",
       "2018    271\n",
       "2019    284\n",
       "2020    273\n",
       "2021     70\n",
       "Name: Year Founded, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making final touches to the column for further analysis\n",
    "complete_set[\"Year Founded\"].apply(str) # Applying string formatting\n",
    "complete_set[\"Year Founded\"].replace(\"-\",\"nan\", inplace = True) # Replacing dashes with nan\n",
    "complete_set[\"Year Founded\"] = complete_set[\"Year Founded\"].apply(lambda x: str(x).replace(\".0\",\"\")) # Removing .0 attached\n",
    "year_founded = ((complete_set.loc[:,\"Year Founded\"]).value_counts())[1:] # Excluding the rows with \"nan\"\n",
    "year_founded = year_founded.sort_index() # Showing trend of startups founded since 2000\n",
    "year_founded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4a68839e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2646</th>\n",
       "      <td>Duroflex</td>\n",
       "      <td>Furniture &amp; Home Decor</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>60000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>5 decades of experience, superior quality and ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1963</td>\n",
       "      <td>P. C. Mathew</td>\n",
       "      <td>Norwest Venture Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>Reliance</td>\n",
       "      <td>Multinational conglomerate company</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance owns businesses across India engaged ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1973</td>\n",
       "      <td>Dhirubhai Ambani</td>\n",
       "      <td>Facebook, Google, KKR, Silver Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>Biocon</td>\n",
       "      <td>Medicine &amp; healthcare</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>15000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Biocon is a healthcare and technology, company...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1978</td>\n",
       "      <td>Kiran Mazumdar-Shaw</td>\n",
       "      <td>Goldman Sachs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>Biocon biologics</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>70000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Biocon is a fully integrated pure play biosimi...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1978</td>\n",
       "      <td>Kiran Mazumdar-Shaw</td>\n",
       "      <td>Goldman Sachs, Tata Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>Arya</td>\n",
       "      <td>Agriculture &amp; agritech</td>\n",
       "      <td>Series B</td>\n",
       "      <td>21000000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>\"Simplifying Agribusiness\" Throughout Agri-Sup...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1982</td>\n",
       "      <td>Chattanathan Devarajan, Prasanna Rao, Anand Ch...</td>\n",
       "      <td>Quona Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379</th>\n",
       "      <td>Five star finance</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>234000000.00</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Five-Star is a Registered Non Banking Finance ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1984</td>\n",
       "      <td>V K Ranganathan</td>\n",
       "      <td>TPG Capital Asia, Epiq Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>Wess</td>\n",
       "      <td>Renewable energy</td>\n",
       "      <td>Seed</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Waaree is India's Largest Solar Module Manufac...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1989</td>\n",
       "      <td>Hitesh Doshi</td>\n",
       "      <td>Centrum Financial Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2645</th>\n",
       "      <td>Vlcc health care</td>\n",
       "      <td>Medicine &amp; healthcare</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>4000000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>VLCC is today widely recognized for its compre...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1989</td>\n",
       "      <td>Vandana Luthra</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>Safex chemicals</td>\n",
       "      <td>Agriculture &amp; agritech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Safex Chemicals India Ltd is a leading company...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1991</td>\n",
       "      <td>SK Jindal, SK Chaudhary</td>\n",
       "      <td>BanyanTree Finance Pvt. Ltd.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Tessolve</td>\n",
       "      <td>Appliances &amp; Electronics</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Tessolve Semiconductor offers engineering in s...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1993</td>\n",
       "      <td>P Raja Manickam, Srinivas Chinamilli, Veerappan V</td>\n",
       "      <td>Novo Tellus Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Credit wise capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>CreditWise Capital is a consumer finance company.</td>\n",
       "      <td>2020</td>\n",
       "      <td>1994</td>\n",
       "      <td>Hirak Joshi, Aalesh Avlani</td>\n",
       "      <td>Venture Catalysts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>Sk finance</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Series F</td>\n",
       "      <td>53000000.00</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>SK Finance is a leading player in vehicle fina...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1994</td>\n",
       "      <td>Rajendra Setia</td>\n",
       "      <td>IIFL Wealth, TPG Growth, Norwest Venture Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Moneyboxx</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Moneyboxx Finance provides easy access to fina...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1994</td>\n",
       "      <td>Deepak Aggarwal</td>\n",
       "      <td>Ashv Finance, BlackSoil Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>Info edge venture</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>Info Edge provides online recruitment, matrimo...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1995</td>\n",
       "      <td>Sanjeev Bikhchandani</td>\n",
       "      <td>Lightbox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>Satya microcapital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>9779825.00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Microfinance company that offers collateral-fr...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1995</td>\n",
       "      <td>Vivek Tiwari</td>\n",
       "      <td>BlueOrchard Finance S A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Pine labs</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>A merchant platform company that provides fina...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1998</td>\n",
       "      <td>Amrish Rau</td>\n",
       "      <td>Mastercard, Temasek Holdings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2217</th>\n",
       "      <td>Pine labs</td>\n",
       "      <td>Information technology</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>A merchant platform company that provides fina...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1998</td>\n",
       "      <td>Amrish Rau</td>\n",
       "      <td>Fidelity, BlackRock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2802</th>\n",
       "      <td>Pine labs</td>\n",
       "      <td>Information technology</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>100000000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>A merchant platform company that provides fina...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1998</td>\n",
       "      <td>Amrish Rau</td>\n",
       "      <td>Invesco Developing Markets Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>Lendingkart</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>15000000.00</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Lendingkart is an online financing company ded...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1999</td>\n",
       "      <td>Harshvardhan Lunia, Mukul Sachan</td>\n",
       "      <td>Bertelsmann India Investments, Fullerton Finan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>Gourmet garden</td>\n",
       "      <td>Consumer goods &amp; services</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Palmwoods</td>\n",
       "      <td>Gourmet Garden is a global market leader in ch...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1999</td>\n",
       "      <td>Arjun Balaji</td>\n",
       "      <td>Advent Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>Nobel hygiene</td>\n",
       "      <td>Medicine &amp; healthcare</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>60000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Nobel Hygiene is one of the leading disposable...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1999</td>\n",
       "      <td>Johari Kamal</td>\n",
       "      <td>Quadria Capital</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Company Name                              Sector Funding Stage  \\\n",
       "2646             Duroflex              Furniture & Home Decor   Undisclosed   \n",
       "940              Reliance  Multinational conglomerate company   Undisclosed   \n",
       "765                Biocon               Medicine & healthcare   Undisclosed   \n",
       "2101     Biocon biologics                       Biotechnology   Undisclosed   \n",
       "644                  Arya              Agriculture & agritech      Series B   \n",
       "2379    Five star finance                             Fintech   Undisclosed   \n",
       "1725                 Wess                    Renewable energy          Seed   \n",
       "2645     Vlcc health care               Medicine & healthcare   Undisclosed   \n",
       "2368      Safex chemicals              Agriculture & agritech   Undisclosed   \n",
       "1679             Tessolve            Appliances & Electronics   Undisclosed   \n",
       "890   Credit wise capital                             Fintech          Seed   \n",
       "1940           Sk finance                  Financial services      Series F   \n",
       "2371            Moneyboxx                             Fintech          Debt   \n",
       "695     Info edge venture                  Financial services   Undisclosed   \n",
       "1162   Satya microcapital                             Fintech          Debt   \n",
       "632             Pine labs                             Fintech   Undisclosed   \n",
       "2217            Pine labs              Information technology   Undisclosed   \n",
       "2802            Pine labs              Information technology   Undisclosed   \n",
       "1721          Lendingkart                             Fintech          Debt   \n",
       "877        Gourmet garden           Consumer goods & services   Undisclosed   \n",
       "2328        Nobel hygiene               Medicine & healthcare   Undisclosed   \n",
       "\n",
       "            Amount Headquarters  \\\n",
       "2646   60000000.00    Bangalore   \n",
       "940  2200000000.00       Mumbai   \n",
       "765    15000000.00    Bangalore   \n",
       "2101   70000000.00    Bangalore   \n",
       "644    21000000.00        Noida   \n",
       "2379  234000000.00      Chennai   \n",
       "1725    2000000.00       Mumbai   \n",
       "2645    4000000.00      Gurgaon   \n",
       "2368   50000000.00    New Delhi   \n",
       "1679   40000000.00    Bangalore   \n",
       "890     6000000.00       Mumbai   \n",
       "1940   53000000.00       Jaipur   \n",
       "2371    3000000.00       Mumbai   \n",
       "695    50000000.00        Noida   \n",
       "1162    9779825.00        Delhi   \n",
       "632           0.00        Noida   \n",
       "2217     600000.00        Noida   \n",
       "2802  100000000.00        Noida   \n",
       "1721   15000000.00      Gujarat   \n",
       "877           0.00    Palmwoods   \n",
       "2328   60000000.00       Mumbai   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "2646  5 decades of experience, superior quality and ...         2021   \n",
       "940   Reliance owns businesses across India engaged ...         2020   \n",
       "765   Biocon is a healthcare and technology, company...         2020   \n",
       "2101  Biocon is a fully integrated pure play biosimi...         2021   \n",
       "644   \"Simplifying Agribusiness\" Throughout Agri-Sup...         2020   \n",
       "2379  Five-Star is a Registered Non Banking Finance ...         2021   \n",
       "1725  Waaree is India's Largest Solar Module Manufac...         2021   \n",
       "2645  VLCC is today widely recognized for its compre...         2021   \n",
       "2368  Safex Chemicals India Ltd is a leading company...         2021   \n",
       "1679  Tessolve Semiconductor offers engineering in s...         2021   \n",
       "890   CreditWise Capital is a consumer finance company.         2020   \n",
       "1940  SK Finance is a leading player in vehicle fina...         2021   \n",
       "2371  Moneyboxx Finance provides easy access to fina...         2021   \n",
       "695   Info Edge provides online recruitment, matrimo...         2020   \n",
       "1162  Microfinance company that offers collateral-fr...         2020   \n",
       "632   A merchant platform company that provides fina...         2020   \n",
       "2217  A merchant platform company that provides fina...         2021   \n",
       "2802  A merchant platform company that provides fina...         2021   \n",
       "1721  Lendingkart is an online financing company ded...         2021   \n",
       "877   Gourmet Garden is a global market leader in ch...         2020   \n",
       "2328  Nobel Hygiene is one of the leading disposable...         2021   \n",
       "\n",
       "     Year Founded                                           Founders  \\\n",
       "2646         1963                                       P. C. Mathew   \n",
       "940          1973                                   Dhirubhai Ambani   \n",
       "765          1978                                Kiran Mazumdar-Shaw   \n",
       "2101         1978                                Kiran Mazumdar-Shaw   \n",
       "644          1982  Chattanathan Devarajan, Prasanna Rao, Anand Ch...   \n",
       "2379         1984                                    V K Ranganathan   \n",
       "1725         1989                                       Hitesh Doshi   \n",
       "2645         1989                                     Vandana Luthra   \n",
       "2368         1991                            SK Jindal, SK Chaudhary   \n",
       "1679         1993  P Raja Manickam, Srinivas Chinamilli, Veerappan V   \n",
       "890          1994                         Hirak Joshi, Aalesh Avlani   \n",
       "1940         1994                                     Rajendra Setia   \n",
       "2371         1994                                    Deepak Aggarwal   \n",
       "695          1995                               Sanjeev Bikhchandani   \n",
       "1162         1995                                       Vivek Tiwari   \n",
       "632          1998                                         Amrish Rau   \n",
       "2217         1998                                         Amrish Rau   \n",
       "2802         1998                                         Amrish Rau   \n",
       "1721         1999                   Harshvardhan Lunia, Mukul Sachan   \n",
       "877          1999                                       Arjun Balaji   \n",
       "2328         1999                                       Johari Kamal   \n",
       "\n",
       "                                              Investors  \n",
       "2646                           Norwest Venture Partners  \n",
       "940                  Facebook, Google, KKR, Silver Lake  \n",
       "765                                       Goldman Sachs  \n",
       "2101                        Goldman Sachs, Tata Capital  \n",
       "644                                       Quona Capital  \n",
       "2379                     TPG Capital Asia, Epiq Capital  \n",
       "1725                         Centrum Financial Services  \n",
       "2645                                                NaN  \n",
       "2368                       BanyanTree Finance Pvt. Ltd.  \n",
       "1679                                Novo Tellus Capital  \n",
       "890                                   Venture Catalysts  \n",
       "1940  IIFL Wealth, TPG Growth, Norwest Venture Partners  \n",
       "2371                    Ashv Finance, BlackSoil Capital  \n",
       "695                                            Lightbox  \n",
       "1162                            BlueOrchard Finance S A  \n",
       "632                        Mastercard, Temasek Holdings  \n",
       "2217                                Fidelity, BlackRock  \n",
       "2802                    Invesco Developing Markets Fund  \n",
       "1721  Bertelsmann India Investments, Fullerton Finan...  \n",
       "877                                     Advent Partners  \n",
       "2328                                    Quadria Capital  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look at which \"startups\" were founded earlier than 2000\n",
    "complete_set.loc[(complete_set[\"Year Founded\"] == \"1963\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1973\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1978\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1982\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1984\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1989\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1991\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1993\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1994\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1995\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1998\") |\n",
    "                (complete_set[\"Year Founded\"] == \"1999\")].sort_values(by = \"Year Founded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4ae141a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# After crosschecking the internet for the founding years of the startups, some mistakes in the data were noted and will be corrected accordingly:\n",
    "## Arya was founded in 2013\n",
    "complete_set.loc[complete_set[\"Company Name\"] == \"Arya\", \"Year Founded\"] = \"2013\"\n",
    "\n",
    "# CreditWise Capital was founded in 2018\n",
    "complete_set.loc[complete_set[\"Company Name\"] == \"Credit wise capital\", \"Year Founded\"] = \"2018\"\n",
    "\n",
    "# Lendingkart was founded in 2014\n",
    "complete_set.loc[complete_set[\"Company Name\"] == \"Lendingkart\", \"Year Founded\"] = \"2014\"\n",
    "\n",
    "# Nobel hygiene was founded in 2001\n",
    "complete_set.loc[complete_set[\"Company Name\"] == \"Nobel hygiene\", \"Year Founded\"] = \"2001\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1a2f8d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Transactions')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_founded.plot()\n",
    "plt.title(\"Number of Deals for Startups per Founding Year\")\n",
    "plt.ylabel(\"Founding Year\")\n",
    "plt.xlabel(\"Number of Transactions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd38248a",
   "metadata": {},
   "source": [
    "Here, we note that generally, newer startups are involved in more funding deals, with an overall increase from startups founded in 2000. 2021's low value may imply lower chances of landing a funding deal in the same year which a startup was founded. As such, it is fair to say that other sources of finance may be used in the first year of founding before actively seeking a funding deal down the line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "29bd6f20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Founded</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>1</td>\n",
       "      <td>60000000.00</td>\n",
       "      <td>60000000.00</td>\n",
       "      <td>60000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>1</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>2200000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>2</td>\n",
       "      <td>85000000.00</td>\n",
       "      <td>42500000.00</td>\n",
       "      <td>42500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1</td>\n",
       "      <td>234000000.00</td>\n",
       "      <td>234000000.00</td>\n",
       "      <td>234000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>2</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>3000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>1</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>50000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>40000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>2</td>\n",
       "      <td>56000000.00</td>\n",
       "      <td>28000000.00</td>\n",
       "      <td>28000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>2</td>\n",
       "      <td>59779825.00</td>\n",
       "      <td>29889912.50</td>\n",
       "      <td>29889912.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>3</td>\n",
       "      <td>100600000.00</td>\n",
       "      <td>33533333.33</td>\n",
       "      <td>600000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>6</td>\n",
       "      <td>226000000.00</td>\n",
       "      <td>37666666.67</td>\n",
       "      <td>20000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2</td>\n",
       "      <td>60000000.00</td>\n",
       "      <td>30000000.00</td>\n",
       "      <td>30000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3</td>\n",
       "      <td>38700000.00</td>\n",
       "      <td>12900000.00</td>\n",
       "      <td>6700000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>4</td>\n",
       "      <td>42000000.00</td>\n",
       "      <td>10500000.00</td>\n",
       "      <td>5500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2</td>\n",
       "      <td>404000000.00</td>\n",
       "      <td>202000000.00</td>\n",
       "      <td>202000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>9</td>\n",
       "      <td>70857220000.00</td>\n",
       "      <td>7873024444.44</td>\n",
       "      <td>10220000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>10</td>\n",
       "      <td>1437060000.00</td>\n",
       "      <td>143706000.00</td>\n",
       "      <td>34000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>34</td>\n",
       "      <td>3356248000.00</td>\n",
       "      <td>98713176.47</td>\n",
       "      <td>12500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>14</td>\n",
       "      <td>246440000.00</td>\n",
       "      <td>17602857.14</td>\n",
       "      <td>15500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>37</td>\n",
       "      <td>2131648000.00</td>\n",
       "      <td>57612108.11</td>\n",
       "      <td>10000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>67</td>\n",
       "      <td>5472230000.00</td>\n",
       "      <td>81675074.63</td>\n",
       "      <td>26500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>72</td>\n",
       "      <td>4493201146.00</td>\n",
       "      <td>62405571.47</td>\n",
       "      <td>8000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>66</td>\n",
       "      <td>2396281500.00</td>\n",
       "      <td>36307295.45</td>\n",
       "      <td>4024750.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>136</td>\n",
       "      <td>4090108400.00</td>\n",
       "      <td>30074326.47</td>\n",
       "      <td>4025000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>275</td>\n",
       "      <td>8375576600.00</td>\n",
       "      <td>30456642.18</td>\n",
       "      <td>5000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>228</td>\n",
       "      <td>3792208733.00</td>\n",
       "      <td>16632494.44</td>\n",
       "      <td>2000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>209</td>\n",
       "      <td>2718563900.00</td>\n",
       "      <td>13007482.78</td>\n",
       "      <td>2000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>272</td>\n",
       "      <td>154479641730.00</td>\n",
       "      <td>567939859.30</td>\n",
       "      <td>1125000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>284</td>\n",
       "      <td>1554576828.00</td>\n",
       "      <td>5473862.07</td>\n",
       "      <td>913500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>273</td>\n",
       "      <td>1176999419.00</td>\n",
       "      <td>4311353.18</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>70</td>\n",
       "      <td>1135630000.00</td>\n",
       "      <td>16223285.71</td>\n",
       "      <td>1500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nan</th>\n",
       "      <td>767</td>\n",
       "      <td>8400797401.00</td>\n",
       "      <td>10952799.74</td>\n",
       "      <td>700000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count             sum          mean        median\n",
       "Year Founded                                                   \n",
       "1963              1     60000000.00   60000000.00   60000000.00\n",
       "1973              1   2200000000.00 2200000000.00 2200000000.00\n",
       "1978              2     85000000.00   42500000.00   42500000.00\n",
       "1984              1    234000000.00  234000000.00  234000000.00\n",
       "1989              2      6000000.00    3000000.00    3000000.00\n",
       "1991              1     50000000.00   50000000.00   50000000.00\n",
       "1993              1     40000000.00   40000000.00   40000000.00\n",
       "1994              2     56000000.00   28000000.00   28000000.00\n",
       "1995              2     59779825.00   29889912.50   29889912.50\n",
       "1998              3    100600000.00   33533333.33     600000.00\n",
       "1999              1            0.00          0.00          0.00\n",
       "2000              6    226000000.00   37666666.67   20000000.00\n",
       "2001              2     60000000.00   30000000.00   30000000.00\n",
       "2002              3     38700000.00   12900000.00    6700000.00\n",
       "2003              1      1000000.00    1000000.00    1000000.00\n",
       "2004              4     42000000.00   10500000.00    5500000.00\n",
       "2005              2    404000000.00  202000000.00  202000000.00\n",
       "2006              9  70857220000.00 7873024444.44   10220000.00\n",
       "2007             10   1437060000.00  143706000.00   34000000.00\n",
       "2008             34   3356248000.00   98713176.47   12500000.00\n",
       "2009             14    246440000.00   17602857.14   15500000.00\n",
       "2010             37   2131648000.00   57612108.11   10000000.00\n",
       "2011             67   5472230000.00   81675074.63   26500000.00\n",
       "2012             72   4493201146.00   62405571.47    8000000.00\n",
       "2013             66   2396281500.00   36307295.45    4024750.00\n",
       "2014            136   4090108400.00   30074326.47    4025000.00\n",
       "2015            275   8375576600.00   30456642.18    5000000.00\n",
       "2016            228   3792208733.00   16632494.44    2000000.00\n",
       "2017            209   2718563900.00   13007482.78    2000000.00\n",
       "2018            272 154479641730.00  567939859.30    1125000.00\n",
       "2019            284   1554576828.00    5473862.07     913500.00\n",
       "2020            273   1176999419.00    4311353.18    1000000.00\n",
       "2021             70   1135630000.00   16223285.71    1500000.00\n",
       "nan             767   8400797401.00   10952799.74     700000.00"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at how much the average startup raised based on their founding year\n",
    "year_set = complete_set.groupby(\"Year Founded\").Amount.agg([\"count\",\"sum\", \"mean\", \"median\"])\n",
    "year_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "706ab681",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Funding Raised by Startups based on Founding Year')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jfnr+eb5tr6pemGnI+c/NpwnAld13ZDrd6t8ynSLyoO6+cGu7BMBm7benXQAAAAD7h/1y5AMAAACw/xA+AAAAAEMduNUd2KyDDjqoDz/88K3uBgAAALDG6aeffkl3H7x2+n4XPhx++OE57bTTtrobAAAAwBpV9bFdTXfaBQAAADDUsPChqp5fVRdV1ZkbLHNMVZ1RVWdV1VtH9QUAAADYOiNHPpyc5Nj1ZlbV9ZM8J8n9uvvWSX5sYF8AAACALTIsfOjutyX5zAaLPCzJq7r74/PyF43qCwAAALB1tvKaD7dKcoOqOrWqTq+qH19vwao6oapOq6rTLr744n3YRQAAAGBvbWX4cGCS2yf5kSQ/lOTXq+pWu1qwu0/q7h3dvePgg7/hFzsAAACAK7Gt/KnN85Nc0t1fTPLFqnpbkqOTfGgL+wQAAACs2FaOfPirJD9QVQdW1bWT3CnJP29hfwAAAIABho18qKqXJDkmyUFVdX6SJye5epJ094nd/c9V9cYkH0jy9STP6+51f5YTAAAA2D8NCx+6+/gllvn9JL8/qg8AAADA1tvK0y4AAACAqwDhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGOrAre4AAAAAVw4X/t75w9q+6S8dOqxtrvyMfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYalj4UFXPr6qLqurM3Sx3h6q6rKoeNKovAAAAwNYZOfLh5CTHbrRAVR2Q5HeTvGlgPwAAAIAtNCx86O63JfnMbhZ7YpJXJrloVD8AAACArbVl13yoqkOS3D/JiVvVBwAAAGC8rbzg5NOS/HJ3X7a7BavqhKo6rapOu/jii8f3DAAAAFiZA7ew9o4kL62qJDkoyQ9X1aXd/Zq1C3b3SUlOSpIdO3b0vuwkAAAAsHe2LHzo7iN23q6qk5O8flfBAwAAALB/GxY+VNVLkhyT5KCqOj/Jk5NcPUm623UeAAAA4CpiWPjQ3cdvYtlHjeoHAAAAsLW28oKTAAAAwFWA8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADDUsfKiq51fVRVV15jrzH15VH5j/3lVVR4/qCwAAALB1Ro58ODnJsRvM/0iSu3X3bZM8NclJA/sCAAAAbJEDRzXc3W+rqsM3mP+uhbvvTnLoqL4AAAAAW+fKcs2Hxyb5663uBAAAALB6w0Y+LKuq7p4pfLjLBsuckOSEJDnssMP2Uc8AAACAVdjSkQ9Vddskz0tyXHd/er3luvuk7t7R3TsOPvjgfddBAAAAYK9tWfhQVYcleVWSR3b3h7aqHwAAAMBYw067qKqXJDkmyUFVdX6SJye5epJ094lJfiPJDZM8p6qS5NLu3jGqPwAAAMDWGPlrF8fvZv7jkjxuVH0AAADgyuHK8msXAAAAwDYlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGGhQ9V9fyquqiqzlxnflXVM6rq3Kr6QFV9z6i+AAAAAFtn5MiHk5Mcu8H8eyc5cv47IckfD+wLAAAAsEWGhQ/d/bYkn9lgkeOS/FlP3p3k+lV101H9AQAAALbGVl7z4ZAkn1i4f/487RtU1QlVdVpVnXbxxRfvk84BAAAAq7GV4UPtYlrvasHuPqm7d3T3joMPPnhwtwAAAIBV2srw4fwkN1+4f2iSC7aoLwAAAMAgWxk+vDbJj8+/enHnJP/W3RduYX8AAACAAQ4c1XBVvSTJMUkOqqrzkzw5ydWTpLtPTHJKkh9Ocm6SLyV59Ki+AAAAAFtnWPjQ3cfvZn4n+ZlR9QEAAIArh6087QIAAAC4ChA+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAx14Hozqup1SXq9+d19vyE9AgAAALaVdcOHJH8w//uAJDdJ8qL5/vFJPjqwTwAAAMA2sm740N1vTZKqemp333Vh1uuq6m3DewYAAABsC8tc8+Hgqvq2nXeq6ogkB4/rEgAAALCdbHTaxU4/n+TUqjpvvn94kp8a1iMAAABgW9lt+NDdb6yqI5N85zzp7O7+ythuAQAAANvFbk+7qKprJ/mfSZ7Q3e9PclhV3Wd4zwAAAIBtYZlrPrwgyVeTfO98//wkvzWsRwAAAMC2skz4cMvu/r0kX0uS7v6PJDW0VwAAAMC2sUz48NWqulaSTpKqumUS13wAAAAAlrLMr108Ockbk9y8ql6c5PuTPGpkpwAAAIDtY5lfu/jbqnpfkjtnOt3iZ7v7kuE9AwAAALaFZX7t4vuTfLm735Dk+kn+V1XdYnTHAAAAgO1hmWs+/HGSL1XV0Zl+cvNjSf5saK8AAACAbWOZ8OHS7u4kxyV5Rnc/Pcn1xnYLAAAA2C6WueDk56vqV5I8Isldq+qAJFcf2y0AAABgu1hm5MNDMv205mO7+1NJDkny+0N7BQAAAGwby/zaxaeS/OHC/Y/HNR8AAACAJa0bPlTVO7r7LlX1+SS9OCtJd/c3D+8dAAAAsN9bN3zo7rvM/7q4JAAAALDHlrngZJKkqm6U5Jo778+nXwAAAABsaLcXnKyq+1XVh5N8JMlbk3w0yV8P7hcAAACwTSzzaxdPTXLnJB/q7iOS3DPJO4f2CgAAANg2lgkfvtbdn05ytaq6Wne/JcntxnYLAAAA2C6WuebD56rqukneluTFVXVRkkvHdgsAAADYLpYZ+XBckv9I8vNJ3pjkX5LcZ2SnAAAAgO1jt+FDd3+xuy/r7ku7+4VJ/ibJ747vGgAAALAdrBs+VNVtq+pvqurMqvqtqrpxVb0yyd8l+eC+6yIAAACwP9to5MNzk/xFkgcmuTjJ+5Kcl+Tbu/uP9kHfAAAAgG1gowtOflN3nzzfPqeqfjHJk7r7svHdAgAAALaLjcKHa1bVdyep+f4Xkty2qipJuvt9ozsHAAAA7P82Ch8uTPKHC/c/tXC/k9xjVKcAAACA7WPd8KG7774vOwIAAABsT7v9qU0AAACAvSF8AAAAAIYaGj5U1bFVdU5VnVtVT9rF/G+pqtdV1fur6qyqevTI/gAAAAD73m7Dh6p68zLTdrHMAUmeneTeSY5KcnxVHbVmsZ9J8sHuPjrJMUn+b1VdY4l+AwAAAPuJdS84WVXXTHLtJAdV1Q1y+U9ufnOSmy3R9h2TnNvd583tvTTJcUk+uLBMJ7ne/POd103ymSSXbnYlAAAAgCuvjX5q86eS/FymoOH0XB4+/HumEQ27c0iSTyzcPz/JndYs86wkr01yQZLrJXlId399ibYBAACA/cRGP7X59CRPr6ondvcz96Dt2sW0XnP/h5KckeQeSW6Z5G+r6u3d/e9XaKjqhCQnJMlhhx22B10BAAAAtspGIx+SJN39zKr6viSHLy7f3X+2m4een+TmC/cPzTTCYdGjk/xOd3eSc6vqI0m+M8l71vThpCQnJcmOHTvWBhgAAADAldhuw4eq+vNMoxLOSHLZPLmT7C58eG+SI6vqiCSfTPLQJA9bs8zHk9wzydur6sZJviPJect2HgAAALjy2234kGRHkqPm0QlL6+5Lq+oJSd6U5IAkz+/us6rq8fP8E5M8NcnJVfVPmU7T+OXuvmRTawAAAABcqS0TPpyZ5CZJLtxs4919SpJT1kw7ceH2BUl+cLPtAgAAAPuPZcKHg5J8sKrek+QrOyd29/2G9QoAAADYNpYJH54yuhMAAADA9rXMr128dV90BAAAANielvm1i89n+nWLJLlGkqsn+WJ3f/PIjgEAAADbwzIjH663eL+qfjTJHUd1CAAAANherrbZB3T3a5LcY/VdAQAAALajZU67eMDC3asl2ZHLT8MAAAAA2NAyv3Zx34Xblyb5aJLjhvQGAAAA2HaWuebDo/dFRwAAAIDtabfXfKiqQ6vq1VV1UVX9a1W9sqoO3RedAwAAAPZ/y1xw8gVJXpvkZkkOSfK6eRoAAADAbi0TPhzc3S/o7kvnv5OTHDy4XwAAAMA2sUz4cElVPaKqDpj/HpHk06M7BgAAAGwPy4QPj0ny4CSfSnJhkgfN0wAAAAB2a5lfu/h4kvvtg74AAAAA29Buw4eqOiLJE5Mcvrh8dwskAAAAgN3abfiQ5DVJ/jTTr1x8fWhvAAAAgG1nmfDhy939jOE9AQAAALalZcKHp1fVk5P8TZKv7JzY3e8b1isAAABg21gmfPiuJI9Mco9cftpFz/cBAAAANrRM+HD/JN/W3V8d3RkAAABg+7naEsu8P8n1B/cDAAAA2KaWGflw4yRnV9V7c8VrPvipTQBgZe7zihcPbf/1D3r40PYBgPUtEz48eXgvAAAAgG1rt+FDd7918X5VfX+ShyV5664fAQAAAHC5ZUY+pKpulylweHCSjyR55cA+AQAAANvIuuFDVd0qyUOTHJ/k00lelqS6++77qG8AAADANrDRyIezk7w9yX27+9wkqaqf3ye9AgAAALaNjX5q84FJPpXkLVX13Kq6Z5LaN90CAAAAtot1w4fufnV3PyTJdyY5NcnPJ7lxVf1xVf3gPuofAAAAsJ/baORDkqS7v9jdL+7u+yQ5NMkZSZ40umMAAADA9rDb8GFRd3+mu/+ku+8xqkMAAADA9rKp8AEAAABgs4QPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYKih4UNVHVtV51TVuVX1pHWWOaaqzqiqs6rqrSP7AwAAAOx7B45quKoOSPLsJPdKcn6S91bVa7v7gwvLXD/Jc5Ic290fr6objeoPAAAAsDVGjny4Y5Jzu/u87v5qkpcmOW7NMg9L8qru/niSdPdFA/sDAAAAbIGR4cMhST6xcP/8edqiWyW5QVWdWlWnV9WPD+wPAAAAsAWGnXaRpHYxrXdR//ZJ7pnkWkn+vqre3d0fukJDVSckOSFJDjvssAFdBQAAAEYZOfLh/CQ3X7h/aJILdrHMG7v7i919SZK3JTl6bUPdfVJ37+juHQcffPCwDgMAAACrNzJ8eG+SI6vqiKq6RpKHJnntmmX+KskPVNWBVXXtJHdK8s8D+wQAAADsY8NOu+juS6vqCUnelOSAJM/v7rOq6vHz/BO7+5+r6o1JPpDk60me191njuoTAAAAsO+NvOZDuvuUJKesmXbimvu/n+T3R/YDAAAA2DojT7sAAAAAED4AAAAAYwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQQ8OHqjq2qs6pqnOr6kkbLHeHqrqsqh40sj8AAADAvjcsfKiqA5I8O8m9kxyV5PiqOmqd5X43yZtG9QUAAADYOiNHPtwxybndfV53fzXJS5Mct4vlnpjklUkuGtgXAAAAYIuMDB8OSfKJhfvnz9P+U1UdkuT+SU7cqKGqOqGqTquq0y6++OKVdxQAAAAYZ2T4ULuY1mvuPy3JL3f3ZRs11N0ndfeO7t5x8MEHr6p/AAAAwD5w4MC2z09y84X7hya5YM0yO5K8tKqS5KAkP1xVl3b3awb2CwAAANiHRoYP701yZFUdkeSTSR6a5GGLC3T3ETtvV9XJSV4veAAAAIDtZVj40N2XVtUTMv2KxQFJnt/dZ1XV4+f5G17nAQAAANgeRo58SHefkuSUNdN2GTp096NG9gUAAADYGiMvOAkAAAAgfAAAAADGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQw0NH6rq2Ko6p6rOraon7WL+w6vqA/Pfu6rq6JH9AQAAAPa9YeFDVR2Q5NlJ7p3kqCTHV9VRaxb7SJK7dfdtkzw1yUmj+gMAAABsjZEjH+6Y5NzuPq+7v5rkpUmOW1ygu9/V3Z+d7747yaED+wMAAABsgZHhwyFJPrFw//x52noem+SvdzWjqk6oqtOq6rSLL754hV0EAAAARhsZPtQupvUuF6y6e6bw4Zd3Nb+7T+ruHd294+CDD15hFwEAAIDRDhzY9vlJbr5w/9AkF6xdqKpum+R5Se7d3Z8e2B8AAABgC4wc+fDeJEdW1RFVdY0kD03y2sUFquqwJK9K8sju/tDAvgAAAABbZNjIh+6+tKqekORNSQ5I8vzuPquqHj/PPzHJbyS5YZLnVFWSXNrdO0b1CQAAANj3Rp52ke4+Jckpa6aduHD7cUkeN7IPAAAAwNYaedoFAAAAgPABAAAAGEv4AAAAAAwlfAAAAACGEj4AAAAAQwkfAAAAgKGEDwAAAMBQwgcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEAAAAYSvgAAAAADCV8AAAAAIYSPgAAAABDCR8AAACAoYQPAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQx241R0AAAAAxrno2a8d2v6NfuZ+u11G+AAAAOwX/t+LLx7a/j0efvDQ9uGqzGkXAAAAwFDCBwAAAGAo4QMAAAAwlPABAAAAGEr4AAAAAAwlfAAAAACGEj4AAAAAQx241R0AAIBVe8grPzS0/Zc98FZD2wfYbox8AAAAAIYSPgAAAABDOe0CAABW5Nde/clhbf/W/Q8Z1jbAaMIHAABg0179ikuGtX3/Bx00rG1gawgfAAAANnDa8y8a1vaOx9xoWNtwZeKaDwAAAMBQRj4AAOxj93vFG4a2/9oH/cjQ9gFgs4x8AAAAAIYy8gEAAOBK5sPP+teh7R/5hBsPbR/WEj7sgYtPfNawtg9+/BOGtQ0AsBUe+Mr3Dmv7lQ+8w7C29xcnvWrcxRCT5IQHuCAisPecdgEAAAAMZeQDAHCVdt9XvHJo+6970AOHtg+wv/vUH541tP2b/I9bD22f5QwNH6rq2CRPT3JAkud19++smV/z/B9O8qUkj+ru943sEwAAAPzr0/9haPs3/tk7fcO0i5755qE1b/TEew5tf28MO+2iqg5I8uwk905yVJLjq+qoNYvdO8mR898JSf54VH8AAACArTHymg93THJud5/X3V9N8tIkx61Z5rgkf9aTdye5flXddGCfAAAAgH1s5GkXhyT5xML985OsHXeyq2UOSXLhwH6xCR955o8Oa/uIJ75mWNuw0zNf/END23/iw980tP3NeOKrjh3a/jMf8Mah7XPlcp9X/umwtl//wMcOa5uN3f+Vbxna/qsfePeh7QOw/6ruHtNw1Y8l+aHuftx8/5FJ7tjdT1xY5g1J/k93v2O+/+Ykv9Tdp69p64RMp2UkyXckOWcPu3VQkkv28LF7YyvqWtftWde6bs+61lXd/b3mVtW1rtuzrnVVd3+vuVV1rev2rLs/rustuvvgtRNHjnw4P8nNF+4fmuSCPVgm3X1SkpP2tkNVdVp379jbdvaHutZ1e9a1rtuzrnVVd3+vuVV1rev2rGtd1d3fa25VXeu6Petup3Udec2H9yY5sqqOqKprJHlokteuWea1SX68JndO8m/d7ZQLAAAA2EaGjXzo7kur6glJ3pTppzaf391nVdXj5/knJjkl089snpvppzYfPao/AAAAwNYYedpFuvuUTAHD4rQTF253kp8Z2Yc19vrUjf2ornXdnnWt6/asa13V3d9rblVd67o961pXdff3mltV17puz7rbZl2HXXASAAAAIBl7zQcAAACApLv3278kz09yUZIzF6YdneTvk/xTktcl+eaFebed5501z7/mPP2NSd4/Tz8xyQGrqpvk4UnOWPj7epLbbbbuCmsePy//gbn+QStc16sneeE8/Z+T/Mou2nvtYluj6yZ5yLyuZyX5vRXWvEaSF8zT35/kmIXH/HaSTyT5wqq3493UHbU9rVtz8Ou60bqemuknd8+Y/260D2qO3Hc2qruZbfjmSd6SaT84K8nPztO/NcnfJvnw/O8NFh7zK5muu3NOpp9H3jn99nN/zk3yjMwj5QbXXHrfWVXdJNdO8oYkZ8/t/M4+fI6X2mdXWXMz++yK1/XULLHPrrjmNTINGf3Q/Po+cB9sT9fLFf//vSTJ0/bBui59fFpx3aWOT5utmeSG8/JfSPKsNW0tdWwaUHep49OqambwsWk367qZ9xMrq7vs8WnF63pqln8/scq6Sx2fVrg9LX1sGrCuSx2fVlxz2HunJPdKcvq8Tqcnucdmj08rrrmpzx1b/bflHdirzid3TfI9ueKb+/cmudt8+zFJnjrfPnDeCI9e2GAPmG/v/ABQSV6Z5KGrqrvmcd+V5LyF+0vXXUXN+Tm4aOdOn+T3kjxlhc/xw5K8dL597SQfTXL4wuMekOQvstyH1L2uO7/GH09y8DzvhUnuuaKaP5PkBfPtG2U6EFxtvn/nJDfN8uHDquoO2Z42qjn4dd1oXU9NsmNfPb8Zv++sV3ez2/BNk3zPfPt6md7UHDX390nz9Ccl+d359lGZ3mB+U5IjkvxLLj8uvifJ987b018nufc+qLn0vrOqupmOGXefl7lGkrevt64D1nepfXaVNTezz654XU/NEvvsimv+ZpLfmm9fLRt/IF/pc7zQ7ulJ7jp4G97U8WmFdZc+Pu1BzeskuUuSx+cbP1QsdWwaUHep49Oqamb8sWmjdd3M+4mV1V32+LTidT01y7+fWGXdpY5Pq35+lzk2rXg7Xvr4tMKao987fXeSm823b5Pkk5s9Pq245qY+d2z135Z3YK9XYPqQufjm/t9z+bUsbp7kg/PtH07yot20dfVM30Y+ZFV11zzmfyf57T2tu7c15zoXJ7nFvFOcmOSEFT7Hx8/rceC8438oybfO866b5B3zjrXbD6mrqJvkDkn+buHxj0zynBXVfHaSRyws9+Ykd1zT1tIHgRXXXen2tFHNwa/rRnVPzZJvFlZRM+P3nfXqbnobXlP/rzKl5eckuek87aZJzplv/0quOFLoTZn+07xpkrMXph+f5E9G1tzTfWeVdefpT0/yk/uybjbxf8/e1swe7LMrqntqNrHPrqjmJ5JcZ7M1V/i6Hjn3Yd1v5ldRM3t4fFpB3T0+Pu2u5sJyj8oVP1Ts8bFpb+qumbep49Mqas7zV3psWnJdN3Vs2tu62cPj017WPDV7cGxaQd09Oj6t6HXd1LFpb+pmL45Pe1Fz6HunNctWkk9nCmeHvXdar+aa6Zs9Nh2eaeTFczONvvibJNdK8pOZvix7f6bw8drz8idnGs3xriTnJXnQnuw32/GaD2cmud98+8cyvcFPklsl6ap6U1W9r6p+afFBVfWmTMnc55O8YoV1Fz0kyUtWWHdTNbv7a0l+OtOQnQsyHdz/dJM1N6r7iiRfTHJhpsTxD7r7M/O8pyb5v5l+UnVPbbbuuUm+s6oOr6oDk/xodv0c7UnN9yc5rqoOrKojMg2z2mzbK687aHvaqObI13V3z/ELquqMqvr1qqqRNffBvrPeuu7xNlxVh2dKyv8hyY27+8Ikmf+90bzYIZnegOx0/jztkPn22ukja+6xVdWtqusnuW+m8Gef1N3sPruCmnu0z67oOd7UPrs3NefXMkmeOv+f//KquvE+XNdkeuP5sp7ftY2quTfHp71c1z06Pi1Zcz17dGxaQd09sqqag45Nu2tj0+8nVlB308enFT3Hm34/sTd19/T4tMJteOlj097W3dPj016u6+j3TosemOQfu/srGfveab2ae+vIJM/u7lsn+dzc9qu6+w7dfXSmcOKxC8vfNNNok/sk+Z09Kbgdw4fHJPmZqjo90zCWr87TD8z0ZD18/vf+VXXPnQ/q7h/K9IR+U5J7rLBukqSq7pTkS9195uL0vay7qZpVdfVMB4DvTnKzTKeh/Moma25U945JLpvbPiLJL1TVt1XV7ZJ8e3e/eg9q7XHd7v5spvV9Wabhih9NcumKaj4/00HltCRPy5QCbrbtldcdtD3tsuY+eF03WteHd/d3JfmB+e+RI2vug31nl3X3dBuuqutmSqt/rrv/faNFdzGtN5g+suYeWVXd+Q3KS5I8o7vP21d1N7PP7m3NPd1nV7Sum9pnV1DzwCSHJnlnd39Ppmut/MFGNVdUd9FDs+YLhxE19/T4tLd19+T4tImam+rLbh+093U3bVU1Bx6bNrTZ9xN7W3dPjk8rWtdNv59YQd1NH59WvA0vdWxaRd09OT7tbc198N5p5/K3TvK7SX5q56RddWdwzb31ke4+Y759eqbRELepqrdX1T9l+tx864XlX9PdX+/uDyZZKtBfa9uFD919dnf/YHffPtOO9S/zrPOTvLW7L+nuLyU5JdP52IuP/XKmi9wct8K6O627o+9p3T2oebv5cf8yp51/meT7NlNzN3UfluSN3f217r4oyTuT7Mg0PPP2VfXRTMPpblVVp+6Duunu13X3nbr7ezMNZfrwKmp296Xd/fPdfbvuPi7J9Tfb9qi6q96eNqg59HXdaF27+5Pzv5/PdG7oHQfXvN08f8i+s5t13dQ2PP9H/8okL+7uV82T/7WqbjrPv2mmb7OS6bi4+G3AoZm+mTh/vr12+siam7biuicl+XB3P20f111qn11RzU3vs6ta183ssyuq+elM357u/CDz8qz5P3/Uus7LHp3kwO4+fR/UvF2yuePTCl/XpY9Pm6y5nk0dm1ZYd1NWXHPUsWm3ln0/saK6mzo+rWpdN/t+YkV1N3V8WuXruuyxaYV1b5csf3xa4es68r1TqurQTK/fj3f34mfNUe+d1qu5txZHT1yWKRg7OckTegrlfjPJNddZfrOjjpNsw/Chqm40/3u1JL+W6dyiZDpH8bZVde05Rb5bkg9W1XUXXuQDM10b4uwV1t057ceSvHRh2l7X3WzNJJ9MclRVHTzfv1em4TSbskHdjye5R02uk+kCKGd39x939826+/BMo04+1N3HjK675jE3SPLfkjxvFTXn7eg68+17ZfqW+oObXadV1R25Pa1Xc/TrusG6HlhVB83Tr55p6NeZu2x8RTUzeN/ZaHvazDZcVZVpOOM/d/cfLsx6bZKfmG//RKZzC3dOf2hVfVNNp3scmeQ9PQ31+3xV3Xlu88cXHjOk5nrrNHpd57Z+K8m3JPm5fVV3M/vsCl/XTe2zK1zXpffZFa5rZzpXfef63TPJusfoAdvx8dnNN4srrLmp49OK952ljk97UHOXNnNsWmXdzVhlzcHHpvXa2dT7iRW+tksfn1a4rpt6P7HCdV36+DRgG97tsWnFdZc+Pq143xn23qmm02bekOlaOO/cufDI907r1RzkekkunPeJh6+89d6DC0VcWf4y7TwXJvlaprTpsUl+NtMFBz+U6VyUWlj+EZkuqHFm5p9dyTRk5L25/OdYnpkpEVxl3WOSvHtNG5uqu4qa8/THZ9rpP5DpwHfDVa1rpgsFvXxenw8m+Z+7aO/wLPerCCupO7fzwflvd79ispmah2dKUv85yd8lucVCO783P/7r879PGV135Pa00boOfl3XW9frZBoatnNdn56NfwZsVa/ryH1no7qb2YbvkmmI3wdy+c9p/XCmC7G+OVPy/+bMF4KdH/OrmUZgnJOFqzJnGj105jzvWcm6Pxe1yppL7zurqpvpm4men/ud7TxuH9Rdep9d5XO8mX12heu69D674u3pFkneNrf15iSH7Yt9Z553XpLv3If769LHpxXXXer4tIc1P5rkM5l+Ru/8JEdt5tg0oO5Sx6dV1cy+OTbtqu5m30+s7Dle9vi0wnXd7PuJVW5PSx2fVv38Zolj04B1Xer4tOKaw947Zfry6IsLy56R+SdaM+i9025qbupzx3r7WZJfTPKUTKesfCTTxVifmeTkef7JWbjIZPbw1zV2vvEFAAAAGGLbnXYBAAAAXLkIHwAAAIChhA8AAADAUMIHAAAAYCjhAwAAADCU8AEArqJq8o6quvfCtAdX1RtXWOOjVfVPVXXG/Pd9q2p7F7WOqarXb/Ixp1bVjlF9AgAmB251BwCArdHdXVWPT/LyqnpLkgOS/HaSY/ekvao6oLsv28Wsu3f3JXvRVQBgP2fkAwBchXX3mUlel+SXkzw5yYuS/GpVvbeq/rGqjkuSqjq8qt5eVe+b/75vnn5MVb2lqv4iyT8tU7OqblFVb66qD8z/HjZPP7mqHrSw3BcWapxaVa+oqrOr6sVVVfO8Y+dp70jygIXHXqeqnr+L9bhWVb10rv2yJNfa2+cQANg9Ix8AgN9M8r4kX03y+iT/r7sfU1XXT/Keqvq7JBcluVd3f7mqjkzykiQ7T1e4Y5LbdPdH1mn/LVV1WZKvdPedkjwryZ919wur6jFJnpHkR3fTx+9OcuskFyR5Z5Lvr6rTkjw3yT2SnJvkZQvL/+o66/FTSb7U3betqtvO6w0ADCZ8AICruO7+4jwK4AtJHpzkvlX1i/PsayY5LNOH/mdV1e2SXJbkVgtNvGeD4CH5xtMuvjeXj1L48yS/t0Q339Pd5ydJVZ2R5PC5vx/p7g/P01+U5IR5+R9Mcr9drMddM4Ud6e4PVNUHlqgNAOwl4QMAkCRfn/8qyQO7+5zFmVX1lCT/muToTKdtfnlh9hf3snbP/146t535tIprLCzzlYXbl+Xy9zCdXVtvPTZ6DAAwiGs+AACL3pTkiQvXVPjuefq3JLmwu7+e5JGZLk65p96V5KHz7Ycnecd8+6NJbj/fPi7J1XfTztlJjqiqW873j1+Yt956vG2umaq6TZLb7tkqAACbIXwAABY9NdOH/g9U1Znz/SR5TpKfqKp3ZzrlYm9GO/z3JI+eT3l4ZJKfnac/N8ndquo9Se60uxrd/eVMp1m8Yb7g5MeWWI8/TnLdufYvJXnPXqwHALCk6jbyEAAAABjHyAcAAABgKOEDAAAAMJTwAQAAABhK+AAAAAAMJXwAAAAAhhI+AAAAAEMJHwAAAIChhA8AAADAUP8/9jzbWkSZBJwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,9))\n",
    "sns.barplot(x = year_set.index, y = year_set[\"sum\"])\n",
    "plt.xlabel(\"Year Founded\")\n",
    "plt.ylabel(\"Amount Raised\")\n",
    "plt.title(\"Total Funding Raised by Startups based on Founding Year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b218d7",
   "metadata": {},
   "source": [
    "Here again, we note that startups founded in 2018 raised the most funds over the period, followed by 2006 and then other startups founded after 2009. This further reinforces the earlier observation that startups may have to find alternative sources of during the early stages, and look out for funding opportunities with time and growth."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da03ba5",
   "metadata": {},
   "source": [
    "#### 3.8.9 Founders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "85f75900",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown Founder         544\n",
       "Byju Raveendran          12\n",
       "Ashneer Grover           10\n",
       "Saurabh Saxena            7\n",
       "Bhavish Aggarwal          6\n",
       "Deepinder Goyal           6\n",
       "Gaurav Munjal             6\n",
       "Sai Srinivas Kiran G      5\n",
       "Falguni Nayar             5\n",
       "Darpan Sanghvi            5\n",
       "Name: Founders, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set[\"Founders\"].apply(str) # Casting the column to string\n",
    "complete_set[\"Founders\"] = complete_set[\"Founders\"].str.split(',').str[0] # Selecting the first name as the primary founder\n",
    "complete_set[\"Founders\"].fillna(\"Unknown Founder\", inplace = True) # Filling nulls with \"Unknown Founder\"\n",
    "complete_set[\"Founders\"].replace(\"  \",\" \", inplace = True) # Removing any double spaces\n",
    "founders = (complete_set.loc[:,\"Founders\"]).value_counts()\n",
    "founders.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "55b8cd4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Known Fundings per Founder')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HkiZJmi7p+EL5AZIm5jZ+X0qAkuZKOkHSfcB2ZSH/BPi67ZcBbL9l+1Tbr1TaN/dmZ+av7+RtfiHpG4U4jpP0/Y5ildQi6SFJ5wBTgHWW8LSHEEKoUdMkVGBP4K+2/wG8KGloLv8iMM72VsCWwNRcvgpwr+0tgQnAYYW6BgM7ArsDpwJI2hXYENgG2AoYJukjkj4I7AvskNtoB0YV2php+8O27ypVLmlVoL/tx6sczzv7Am8ABwMfBrYFDpO0NXBZbrvkC8CVHcWat9kYuMj21rafKDYo6XBJbZLa2l+fUyW0EEIIXdVMCXV/UoIhf98/L08CDpZ0HLC57Vdz+VvAjXl5MtBSqOs62wtsPwgMymW75q/7Sb27TUhJ62PAMGCSpKn59QZ5n3bg6gqxCvA7L6RP5t7tLEnbV9h3R+Ba26/ZngtcA+xk+35gTUlrSdoSeMn2v6vECvCE7XsrxITt0bZbbbf26Teg0iYhhBAWU1N8bEbS6sAuwGaSDPQBLOmHtifk3tlngIslnWb7IuBt26Wk1s6ixzqvWH3h+ym2f1/W9hHAhbZ/XCG0N223lxfafkXSa5LWt/247XHAOEk3AitW2FfldRRcBYwE/oeFbyg6irUFeK1KXSGEELpJs/RQR5KGMdez3WJ7HeBxYEdJ6wGzbY8B/gAMrVZRFeOAQ/K1WiQNkbQmcBswMi8jabXcZmdOAc6V9N68n4CVO9h2ArCnpH6SVgH2Au7M6y4jXTMeSUqu1WINIYTQIE3RQyUN755aVnY16frpvcAPJL0NzAW+vDgN2L45Xy+9J+U+5gIH2H5Q0k+Bm/Nds28D3wSe6Lg2AM4F+gH3SZqX67ubNExb3vYUSWOBibnovDzci+0H8jXZp20/Wy1WUk88hBBCA2jhqGhYlrS2trqtra3RYYQQQlORNNl2a6V1zTLkG0IIIfRqkVBDCCGEOoiEGkIIIdRBJNQQQgihDiKhhhBCCHUQCTWEEEKog0ioIYQQQh1EQg0hhBDqIBJqCCGEUAeRUEMIIYQ6aJZn+YY6m/H0HFqOvqnRYYTQrWad+plGhxCWIdFDDSGEEOogEmoIIYRQB706oUr6iaQHJE2XNFXSh7u5vRZJMztYN0vSwE727yfpEkkzJM2UdFdpztIQQghLt157DVXSdsDuwFDb83IyW7EO9S5ve/4SB1jZt4HnbG+e29qYNH9qCCGEpVxv7qEOBl6wPQ/A9gu2nwGQdIykSbkXOFp5lm1J4yW15uWBkmbl5YMkXSnpBtJE4f0l3SZpSu5N7lFod3lJF+Ze8VWS+hXWHVHYZ5MOYn669ML2I6X4JV0naXLucR+ey9aT9GiOdTlJd0raVVIfSaflY5wu6at5+xH5GK+S9HDuDZeOveI5CSGE0DN6c0K9GVhH0j8knSPpo4V1Z9kebnszoC+pJ9uZ7YADbe8CvAnsZXsosDNweiEBbQyMtr0F8ArwjUIdL+R9zgWOqtDG+cCPJN0j6eeSNiysO8T2MKAVOFLS6rafAH4B/A74PvCg7ZuBQ4E5tocDw4HDJK2f69ka+A7wIWADYIdaz4mkwyW1SWprf31ODacshBBCrXptQrU9FxgGHA48D1wu6aC8emdJ90maAewCbFpDlbfYfjEvCzhZ0nTgVmAIMCive9L23Xn5j8COhTquyd8nAy0VYp5KSnKnAasBkyR9MK8+UtI04F5gHWDDvM95wKrA11iYpHcFvixpKnAfsHppe2Ci7adsLwCmFuLo9JzYHm271XZrn34DKp+lEEIIi6XXXkMFsN0OjAfG50RxoKTLgHOAVttPSjoOWDnvMp+FbxJWLqvutcLyKGANYJjtt/PQcGl7l4dRWJ6Xv7fTwbnLbwSuAa6RtAD4tKRBwMeB7Wy/Lml8qb08pLx23r0/8Cop4R9he1yxbkkjCjG8E4eklen4nIQQQugBvbaHKmnjsiHTrYAnWJgoXsh30I4sbDOL1KulrLzcAGB2TqY7A+sV1q2bb4gC2B+4qwsx7yDpfXl5RdKw7BO5vZdyMt0E2Law2y+AS4BjgDG5bBzwdUkr5Lo2krRKlaarnZMQQgg9oDf3UPsDv5X0XlLP85/A4bZfljQGmEFKoJMK+/wSuELSl4C/Van7EuAGSW2kYdOHC+seIvWEfw88SrpeWqv3A+fm67HLATcBV5PuTv5aHmJ+hDTsS74uPBzYwXa7pL0lHQycRxrKnZLreh7Ys6NGOzknIYQQeoDs8hHOsCxobW11W1tbo8MIIYSmImmy7dZK63rtkG8IIYTQTCKhhhBCCHUQCTWEEEKog0ioIYQQQh1EQg0hhBDqIBJqCCGEUAeRUEMIIYQ6qJpQ8wwoFecHDSGEEMJCVRNqfgD7NEnr9lA8IYQQQlOq5dGDg4EHJE2k8IB525/rtqhCCCGEJlNLQj2+26MIPW7G03NoOfqmRocRllGzTv1Mo0MIoe46Tai275C0HrCh7VvzdGN9uj+0EEIIoXl0epevpMOAq4Df56IhwHXdGFOXSBok6U+SHpM0WdI9kvZqdFwAko6TZEkfKJR9N5dVfLhyDXWuJemqTrZpiZvJQgihZ9XysZlvAjsArwDYfhRYszuDqlWe2uw6YILtDWwPA/Zj4YTdS1p/PXriM0gxlYwEHlzcymw/YzvmOw0hhF6mloQ6z/ZbpReSlgd6y5xvuwBv2f5dqcD2E7Z/C+/01O6UNCV/bZ/LR0i6sbSPpLMkHZSXZ0k6RtJdwD6SDpM0SdI0SVdL6idpQN5uubxPP0lPliYEL3MdsEfebgNgDml+01LbcwvLIyWNzctjJZ0p6e+59z2ycEwzqx1fCCGEnldLQr1D0v8D+kr6BHAlcEP3hlWzTYEpVdbPBj5heyiwL3BmjfW+aXtH25cB19gebntL0uTjh9qeA0wDPpq3/ywwzvbbFep6BXhS0mbA/sDlNcYA6Q7rHYHdgVMrrO/S8Uk6XFKbpLb21+d0IYwQQgidqSWhHk3qUc0Avgr8Gfhpdwa1uCSdnXuSk3LRCsAYSTNIbwQ+VGNVxaS3We4FzgBGkZJ4aZt98/J+VE+Ul+Vt9gSurTEGgOtsL7D9IDCowvouHZ/t0bZbbbf26TegC2GEEELoTC13+S4AxuSv3uYBYO/SC9vflDQQaMtF3wWeA7YkvXl4M5fPZ9E3EyuX1ftaYXkssKftaXlYeEQuvx44RdJqwDDgb1XivAE4DWiz/Uq69PuO4vB5eRzzCsvi3To6vhBCCD2sw4Saez0dXiu1vUW3RNQ1fwNOlvR12+fmsn6F9QOAp2wvkHQgCz/u8wTwIUkrkZLYx4C7OmhjVeDZfH10FPA0gO25+WEXZwA32m7vKEjbb0j6EfCPCqufk/RB4BFgL+DVTo+68+MLIYTQw6r1UHfP37+Zv1+cv48CXu+2iLrAtiXtCfxa0g9JQ9OvAT/Km5wDXC1pH+D2vA7bT0q6ApgOPArcX6WZnwH3kZLwDFKCLbmcNNQ6ooZYL+tg1dHAjcCTwEygf2d1sfCNTsXjCyGE0PNkV79hV9LdtnforCz0DEnDgF/Z/minG1ex0uANPfjA39QnqBC6KJ6UFJqVpMm2Kz5HoJZHD64iaUfbd+XKtgdWqWeAoTb5YRB/IvVql8jmQwbQFv/UQgihbmpJqIcC50sq3Rb6MnBIt0UUOmS7Ddio0XGEEEJ4t1ru8p0MbCnpPaQh4vgAYwghhFCm04Sa74TdG2gBli995MP2Cd0aWQghhNBEahny/T/S4/Ims+jnIkMIIYSQ1ZJQ17b9qW6PJIQQQmhitTx68O+SNu/2SEIIIYQmVksPdUfgIEmPk4Z8RXqmQm94UlIIIYTQK9SSUHfr9ihCCCGEJldLQu0tc5+GEEIIvVYtCfUmUlIV6UHy65Me5L5ptZ1CCCGEZUktD3ZY5IYkSUNJ86KGJjbj6Tm0HH1To8MIy6h4lm9YGtVyl+8ibE8BhndDLCGEEELTquVJSd8rvFwOGEqaJq1bSGonTZO2Amki8AuB3+SJzuvZznmkWVse7MI+c23XMr1aR/svD5wA7MPCqdautH3S4tbZQTtLFGcIIYSuq+UaanH+z/mka6pXd084ALxheysASWuSZlcZABxbz0Zsf6We9ZVTekajyt4I/Bz4H2Bz229KWhX4fnfGEUIIoWd0OuRr+3jbxwO/As6wfYntN7s/NLA9Gzgc+JaSPpJOkzRJ0nRJ71zLlfSDQvnxuaxF0sOSLszlV0nql9eNz9OhIWmupJMkTZN0r6RBuXx9Sffkek8sxlalvYcknQNMAdYpbN8POAw4onT+bL9q+7jCNt+TNDN/fSeXnSjp24VtTpJ0pKT+km6TNEXSDEl71O/MhxBC6KpOE6qkzSTdD8wEHpA0WdJm3R9aYvsxUpxrkqaSm2N7OOk67mE56e0KbAhsA2wFDJP0kVzFxsDo/CCKV4BvVGhmFeBe21sCE0iJD+AM4Nzc3n9KG9fQ3kW2t7b9RKGNDwD/tv1qpePME4cfDHwY2DYf29bAH4AD8zbLAfsBlwBvAnvZHgrsDJyu0swFHZB0uKQ2SW3tr8ekQSGEUE+13JQ0Gvie7fVsr0caohzdvWG9SylR7Ap8WdJU4D5gdVJi2zV/3U/qGW6SywGetH13Xv4j6clP5d4CbszLk0kz6wDsAFyaly8ubF+tvSds39vpAUkHS5oq6UlJ6+S4rrX9mu25wDXATrZnAf/NyXVX4H7b/83n5GRJ04FbgSHAoGpt2h5tu9V2a59+A6ptGkIIoYtquYa6iu3bSy9sj5e0SjfGtAhJGwDtwGxSEjnC9riybT4JnGL792XlLbz7wRSVHlTxtu1SeTuLnpdK26tKe69V2B7gn8C6klbNQ70XABdImgn0YeGbhkrOAw4iXX89P5eNAtYAhtl+W9Is0ueEQwghNEAtPdTHJP0sXx9skfRT4PHuDgxA0hrA74CzcsIbB3xd0gp5/UY5uY8DDpHUP5cPyTc0QUpi2+Xl/YG7uhDC3aQhVkgJrKRaexXZfp00fHuWpJXzfn2AFfMmE4A9JfXLx7QXcGdedy3wKdIwd+nNxABgdk6mOwPrdeG4Qggh1FktPdRDgONJQ5CQ/vEf3G0RQd88pFv62MzFpBuiIPXUWoAp+Xrh88Cetm+W9EHgnnwZcS5wAKm3+RBwoKTfA48C53Yhlm8Df8o3Bb1zZ3Mn7VXzE+BEYKakV4E3SB8Lesb2LEljgYmlY7V9f27vLUm3Ay/bLrVxCXCDpDZgKvBwF44rhBBCnWnhSGfZCunztq/Jy++z/VKPRlYHeQj2Rts9dhNVd8g3I00B9rH9aD3qXGnwhh584G/qUVUIXRZPSgrNStJk262V1lXrof6Uhb3S20gPdAg9TNKHSDdMXVuvZAqw+ZABtMU/tRBCqJtqCVUdLDeNfIdsU/dO85OcNmh0HCGEEKqrllD75o9qLAesnJffSaz5mb4hhBBCoHpCfZaFNwP9p7AM6aMku3RXUCGEEEKz6TCh2t65JwMJIYQQmlmXp28LIYQQwrtFQg0hhBDqoMOEKmmH/H2lngsnhBBCaE7Veqhn5u/39EQgIYQQQjOrdpfv25IuAIZIOrN8pe0juy+sEEIIoblUS6i7Ax8nfTxmcs+EE0IIITSnDp/l+84G0pa2p/VQPKGHxLN8QyPFs3xDs6r2LN9a7vL9r6RrJc2W9JykqyWtXecYQwghhKZWS0K9ALgeWAsYAtyQy5aIpHZJUyVNkzRF0va5fISkG5e0/lzXefnh8h2tHy+p4juNCtt+V9KbkgbUI7buJGluo2MIIYRlTS0JdU3bF9ien7/GAmvUoe03bG9le0vgx8ApdahzEba/kh8uXw/7A5NIE393K0m1zFMbQgihF6kloT4v6QBJffLXAcB/6xzHe4DifKv9JV0l6WFJl+TJxJF0jKRJkmZKGq3kg5JKk3IjqUXS9Lw8XlJrjnts3m+GpO8W2tpH0kRJ/5C0U6XgJL0f6E+a0m7/Qnk/SVdImi7pckn3lXq8kg7NdY6XNEbSWbn8s3m7+yXdKmlQLj8uH9PNwEWS/ixpi7zufknH5OUTJX1FUn9Jt+Xe/QxJeyzhzyCEEMISqKUndAhwFvBr0kPx/57LllRfSVOBlYHBLPqw/a2BTYFngLuBHYC7gLNsnwAg6WJgd9s3SFpR0ga2HwP2Ba4oa2srYEhponFJ7y2sW972NpI+DRxLurO53P7ApcCdwMaS1rQ9G/gG8JLtLSRtBkzN9a8F/Iw0h+yrwN+A0o1ddwHb2rakrwA/BL6f1w0DdrT9hqSjgZ0kzQLm53MAsCPwR+BNYC/br0gaCNwr6XpXuctM0uHA4QB93lOPQYYQQgglnfZQbf/b9udsr2F7Tdt72n6iDm2Xhnw3AT5F6pWVpoebaPsp2wtISaoll++ce3czSAl401x+BfCFvLwvcHlZW48BG0j6raRPAa8U1pUmUZ9caKfcfsBlOZ5rgH1y+Y7AZQC2ZwLTc/k2wB22X7T9NnBloa61gXH5GH5QOAaA622/kZfvBD6S27iJ1GvvB7TYfoQ0ld7JuTd+K+n69qAO4ifHONp2q+3WPv16/aXgEEJoKr3iWb627wEGsvDa7LzC6nZgeUkrA+cAI21vDowh9W4hJdAvSNooVedHy+p/CdgSGA98EzivsLrUVjsVeux52HVD4JbcW9yPhcO+HU28Xm1C9t+SetqbA18tHAPAa4XlSUArsBMwAbgfOIyFnwkeRTpfw2xvBTxXVlcIIYQe1CsSqqRNgD5UvzZbShYvSOoPjCytsP0vUkL8Ge/unZKHRJezfTULh2JrtT9wnO2W/LUW6elR65GGb7+Q2/gQsHneZyLwUUnvyzcY7V2obwDwdF4+sKNGbb8FPJnrv5fUYz0qfy/VM9v225J2BtbrwjGFEEKos0beTVq6hgqpR3eg7faFo76Lsv2ypDHADGAWqQdXdDlwGrB+hd2HABdIKr2B+HEX4twP2K2s7NpcfhZwYR52vZ805DvH9tOSTgbuI10HfhCYk/c9DrhS0tOkRFkp3pI7gY/Zfl3SnaTh4lJCvQS4QVIbaVj84S4cUwghhDqr5UlJg4CTgbVs75Z7YtvZ/kNPBNibSeoDrGD7zXwn8G3ARrbfktTf9tzcQ70WON/2tQ0NuKC1tdVtbW2NDiOEEJqKlvBJSWOBcaQHOwD8A/hOXSJrfv2AuyRNIyXNr+ehWoDjcg98JvA4cF1DIgwhhNAjahnyHWj7Ckk/BrA9X1J7N8fVFGy/SrpxqNK6o3o4nBBCCA1USw/1NUmrkz6DiqRtWXg9MIQQQgjU1kP9HulZvu+XdDfpoxojq+8SQgghLFs6Tai2p0j6KLAx6W7cR/LDCkIIIYSQdZpQ852snyY9RWh5YFdJ2P5VN8cWQgghNI1ahnxvID03dgawoHvDCSGEEJpTLQl1bdtbdHskIYQQQhOr5S7fv0jatdsjCSGEEJpYLT3Ue4Fr82P73ibdmGTb7+nWyEIIIYQmUktCPR3YDphRba7N0FxmPD2HlqNvanQYoReZdepnGh1CCE2tliHfR4GZkUxDCCGEjtXSQ30WGC/pLxTmKY2PzYQQQggL1dJDfZw0i8qKwKqFr7qRZEkXF14vL+l5STd2sZ7xklrz8tx6xlhD2z3aXlnb7ZKmSpop6QZJ721ULCGEsKyq5UlJx/dAHK8Bm0nqa/sN4BMsnIQ7dO4N21sBSLoQ+CZwUkMjCiGEZUynPVRJa0g6TdKfJf2t9NUNsfwFKN0VsT9waSGGbST9XdL9+fvGubyvpMskTZd0OdC3LPaTJE2TdG+e1xVJYyWNLGwzN38fLGlCoae3U3F9Xh4paWxeXl/SPZImSTqxsE1/SbdJmiJphqQ9cnmLpIckjZH0gKSbJfXN6w7L9UyTdLWkfoVYz8zH/Fgx7iruIU2oHkIIoQfVMuR7CfAwsD5wPDALmNQNsVwG7CdpZWAL4L7CuoeBj9jeGjiGNOE5wNeB1/ODJ04ChhX2WQW41/aWwATgsE7a/yIwLvf0tgSmdrL9GcC5tocD/ymUvwnsZXsosDNwuiTldRsCZ9veFHgZ2DuXX2N7eI71IeDQQn2DgR2B3YFTqwWUHxP5MdJkBpXWHy6pTVJb++sxYVAIIdRTLQl1ddt/AN62fYftQ4Bt6x2I7emk5wXvD/y5bPUA4EpJM4FfA5vm8o8AfyzsP72wz1tA6Rrs5Fx3NZOAgyUdB2ye5zqtZgcW9qIvLpQLOFnSdOBWUm9xUF73uO2pFWLaTNKdkmYAowrHB3Cd7QW2HyzUU65vnsz8v8BqwC2VNrI92nar7dY+/QZ0cnghhBC6opaEWppZ5llJn5G0NbB2N8VzPfBLCsO92YnA7bY3Az4LrFxY19HHed4ufNSnnYXXi+eTjzv3HFcEsD2BlKCfBi6W9OUK9Rfb7ajtUaQp7obl3u5zhf3mFbYrxjQW+JbtzUmjAMV2ivuIykrXUNfLx/PNDrYLIYTQTWpJqD+XNAD4PnAUcB7w3W6K53zgBNszysoHsPAmpYMK5RNICQxJm5GGijszi4VDw3sAK+T91wNm2x4D/AEYmrd5TtIH85Oi9irUczewX14eVRbrbNtvS9qZlOQ6syrpDcsKZXV1ie05wJHAUbmuEEIIPaTThGr7RttzbM+0vbPtYbYrXqNbUrafsn1GhVX/C5ySJzjvUyg/F+ifh1d/CEysoZkxwEclTQQ+TLrDGGAEMFXS/aRrm6U4jiYNHf+N9Jnckm8D35Q0iZRESy4BWiW1kZLjwzXE9DPSNeNbaty+Q7bvB6axMNmHEELoAeroAUiSjqmyn22fWGV96OVaW1vd1tbW6DBCCKGpSJpsu7XSumqfQ32tQtkqpDtQVydd1wwhhBACVRKq7dNLy5JWJQ1xHkz6eMvpHe0XQgghLIuqPilJ0mrA90jXAi8Ehtp+qScCCyGEEJpJhwlV0mnA54HRpM9lNuxZtSGEEEJvV+0u3+8DawE/BZ6R9Er+elXSKz0TXgghhNAcql1DreUzqiGEEEKgtgc7hBBCCKETkVBDCCGEOoiEGkIIIdRBJNQQQgihDqp+DjUsvWY8PYeWo29qdBihF5l16mcaHUIITS16qCGEEEId9PqEKqld0lRJD0iaJul7eSq1hpO0nKQzJc2UNEPSJEnr17mNljyxelf2OU/Sh+oZRwghhOqaYci3NHk2ktYE/kSaLu3YWnaW1Md2ezfFti/p4Rdb2F4gaW0qTyrQo2x/pdExhBDCsqZX9PRqZXs2cDjwLSUtku6UNCV/bQ8gaYSk2yX9CZiRt3tY0oWSpku6SlK/vO0xuWc5U9JoScrl4yX9QtJESf+QtFOFkAYDz9pekON7qvSsY0nnSmrLPevjSztImiXp+BzvDEmb5PI1JN2Sy38v6QlJA/NufSSNyXXdLKlvnvR8YqHeljwvbCn2itMLhRBC6B5NlVABbD9GintNYDbwCdtDSb3FMwubbgP8xHZp6HNjYLTtLYBXgG/k8rNsD7e9GdAX2L1Qx/K2twG+Q+Ue8RXAZ/OQ9OmSti6s+0meM28L0oTmWxTWvZBjPhc4KpcdC/wtl18LrFvYfkPgbNubAi8De9t+CFhR0gZ5m31zPB2SdHhO8m3tr8+ptmkIIYQuarqEmil/XwEYI2kGcCVQvG440fbjhddP2r47L/8R2DEv7yzpvlzHLsCmhX2uyd8nAy3lQdh+ipSofwwsAG6T9LG8+guSpgD35zqLsVWqd0fS1HjY/itQnNXncdtTK+xzBfCFvLwvcHl5jGXxjrbdaru1T78B1TYNIYTQRc1wDXURuUfWTuqdHgs8B2xJenPwZmHT8muZLn8taWXgHKDV9pOSjgNWLmwzL39vp4NzZXse8BfgL5KeA/aU9Bip5znc9kuSxtZQr+jYvMJyO6knDSmBXinpmhSKH61SRwghhG7UVD1USWsAvyMN05p0c1LpGuaXgD5Vdl9X0nZ5eX/gLhYmuRck9QdGdjGeoZLWysvLkYZ3nwDeQ0rocyQNAnarobq7yL1NSbsC7+tsB9v/IiXYn9FJ7zSEEEL3aoYeal9JU0nDu/OBi4Ff5XXnAFdL2ge4nep32D4EHCjp98CjwLm2X5c0BpgBzAImdTG2NUlDzivl1xNJyf5NSfcDDwCPAXd3VEHB8cClkvYF7gCeBV4F+ney3+XAaUBdP64TQgiha5Q6eks3SS3AjfnGo14pJ+V22/NzT/rc0seFusNKgzf04AN/013VhyYUT0oKoXOSJucbTt+lGXqoy4p1gSvy0PFbwGHd2djmQwbQFv9AQwihbpaJhGp7FtBre6cA+YairTvdMIQQQq/UVDclhRBCCL1VJNQQQgihDiKhhhBCCHUQCTWEEEKog0ioIYQQQh1EQg0hhBDqIBJqCCGEUAeRUEMIIYQ6iIQaQggh1MEy8aSk8G4znp5Dy9E3NTqM0IvEs3xDWDLRQw0hhBDqYKlLqJLaJU0tfLVU2XZuN8Xw9w7KLen0wuuj8qTmIYQQmtzSOOT7RndOe1YL29t3sGoe8HlJp9h+obvjkLS87fnd3U4IIYSlsIdaTlJ/SbdJmiJphqQ9KmyznKRzJD0g6UZJf5Y0Mq+bJWlgXm6VND4vHyfpfEnjJT0m6chCfR31fOcDo4HvVojhs5Luk3S/pFslDcpxPSppjUKc/5Q0sNL2hbhGS7oZuGiJTl4IIYSaLY0JtW9huPda4E1gL9tDgZ2B0yWpbJ/PAy3A5sBXgO1qbGsT4JPANsCxklaoYZ+zgVGSBpSV3wVsa3tr4DLgh7YXAH8ERuVtPg5My73bd21fqGsYsIftLxYbkHS4pDZJbe2vz6nxEEMIIdRiqR/yzUnuZEkfARYAQ4BBwH8K++wIXJkT2H8k3V5jWzfZngfMkzQ71/tUtR1svyLpIuBI4I3CqrWByyUNBlYEHs/l5wP/B/wGOAS4oJPtAa63Xay71PZoUg+ZlQZv6BqPMYQQQg2Wxh5quVHAGsCwnGifA1Yu26a8x1o0n4XnqXy/eYXldmp/g/Ib4FBglULZb4GzbG8OfLXUlu0ngeck7QJ8GPhLte2z12qMI4QQQp0sCwl1ADDb9tuSdgbWq7DNXcDe+RrlIGBEYd0s0hAqwN71CMj2i8AVpKRajPPpvHxg2S7nkYZ+r7DdXsP2IYQQetiykFAvAVoltZF6qw9X2OZq0lDtTOD3wH1A6SLj8cAZku4k9ULr5XRgYOH1ccCVuZ3yO4CvB/qzcLi3s+1DCCH0MNlxKQ3S3cC250paHZgI7GD7P53t1xMktQK/tr1TvepcafCGHnzgb+pVXVgKxJOSQuicpMm2WyutWxpvSlpcN0p6L+kGnxN7UTI9Gvg6C+/0rYvNhwygLf6BhhBC3URCzWyPaHQMldg+FTi10XGEEEKoblm4hhpCCCF0u0ioIYQQQh1EQg0hhBDqIBJqCCGEUAeRUEMIIYQ6iIQaQggh1EEk1BBCCKEOIqGGEEIIdRAPdlhGzXh6Di1H39ToMEIvEo8eDGHJRA81hBBCqINIqCGEEEIddFtClfQTSQ9Imi5pqqQPd7L9CZI+XqF8W0n35ToeknRcB/u3Sjqzhrj+XvNBdJGk8XlmGCS1SHpU0idrjW0J2v2epIclzZA0TdKvJK3QXe2FEEJ4t265hippO2B3YKjteZIGkmZx6ZDtYzpYdSHwBdvTJPUBNq7Q3vK224C2zmKzvX2nB7CEJK0NjAO+b3tcLn5XbDnu+UvY1teAXYFtbb8saUXge0Bf4O0lqTuEEELtuquHOhh4wfY8ANsv2H4GQNIxkiZJmilptCTl8rGSRlaoa03g2VxPu+0H8/bH5f1vBi6SNELSjYV15+ce42OSjixVJmlu/n65pE8XysdK2jv3LO+UNCV/bZ/XD5Y0IfeUZ0rqaG7S/wFuBn5q+/q8b3lsxbg7am9Ejv+q3Pu8pHSuyvwE+Lrtl/M5esv2qbZf6eRnFEIIoY66K6HeDKwj6R+SzpH00cK6s2wPt70ZqRe1eyd1/Rp4RNK1kr4qaeXCumHAHra/WGG/TYBPAtsAx1YYAr0M2Bcg9+o+BvwZmA18wvbQvL40VPtFYJztrYAtgakdxHtRPsYrqxxTMe6O2gPYGvgO8CFgA2CHYiWSVgX62368SlvF7Q+X1Caprf31ObXsEkIIoUbdklBtzyUljcOB54HLJR2UV++cr4nOAHYBNu2krhOAVlKS/iLw18Lq622/0cGuN9meZ/sFUtIaVLb+L8AuklYCdgMm5LpWAMbk+K4kJTOAScDB+Rru5rZf7aDdW4EvSepX5bCKcXfUHsBE20/ZXkBK4C1l9QjwOy/S9dqpkmaVerpFtkfbbrXd2qffgCrhhRBC6KpuuykpD8+Ot30s8C1g79y7PAcYaXtzYAywcrV6cl3/sn0uqRe5paTV86rXquw2r7DcTtn1YttvAuNJvdh9ST1WgO8Cz5F6oa3ka7+2JwAfAZ4GLpb05Q7a/V/gPuBKSR1doy7GXbG9Go/hFeA1Sevn16Ue9Ew6uWYdQgihvroloUraWNKGhaKtgCdYmDxfkNQfqHTNtLyuzxSuHW5ISiwv1ynUy4CDgZ1INxEBDACezb3CLwF9chzrAbNtjwH+AAytUu93gVeAP3Rw3bOoYntdcApwrqT35jhFDW9SQggh1Fd3PSmpP/Db/E9+PvBP4PB8F+oYYAYwizSM2pkvAb+W9Hqua5Tt9s7zVE1uJl3zvN72W7nsHOBqSfsAt7OwNzkC+IGkt4G5QEc9VGxb0oHAjaQea7VHEnXUXq3OBfoB90mal2O7G7i/i/WEEEJYArLd+VZhqdPa2uq2tk4/ZRRCCKFA0mTbrZXWxZOSQgghhDqIhBpCCCHUQSTUEEIIoQ4ioYYQQgh1EAk1hBBCqIO4y3cZJelV4JFGx1FnA4EXGh1EN1gajyuOqTnEMb3berbXqLSiuz6HGnq/Rzq69btZSWpb2o4Jls7jimNqDnFMXRNDviGEEEIdREINIYQQ6iAS6rJrdKMD6AZL4zHB0nlccUzNIY6pC+KmpBBCCKEOoocaQggh1EEk1BBCCKEOIqEugyR9StIjkv4p6ehGx7OkJK0j6XZJD0l6QNK3Gx1TvUjqI+l+STc2OpZ6kPReSVdJejj/vLZrdExLStJ38+/dTEmXSmrK+YglnS9ptqSZhbLVJN0i6dH8/X2NjLGrOjim0/Lv33RJ15bmkq6HSKjLGEl9gLOB3YAPAftL+lBjo1pi84Hv2/4gsC3wzaXgmEq+DTzU6CDq6Azgr7Y3AbakyY9N0hDgSKDV9mZAH2C/xka12MYCnyorOxq4zfaGwG35dTMZy7uP6RZgM9tbAP8AflyvxiKhLnu2Af5p+7E8qfplwB4NjmmJ2H7W9pS8/Crpn/SQxka15CStDXwGOK/RsdSDpPcAHwH+AGD7LdsvNzSo+lge6CtpeaAf8EyD41ksticAL5YV7wFcmJcvBPbsyZiWVKVjsn2z7fn55b3A2vVqLxLqsmcI8GTh9VMsBcmnRFILsDVwX4NDqYffAD8EFjQ4jnrZAHgeuCAPY58naZVGB7UkbD8N/BL4N/AsMMf2zY2Nqq4G2X4W0htXYM0Gx1NvhwB/qVdlkVCXPapQtlR8dkpSf+Bq4Du2X2l0PEtC0u7AbNuTGx1LHS0PDAXOtb018BrNN4S4iHxNcQ9gfWAtYBVJBzQ2qlALST8hXS66pF51RkJd9jwFrFN4vTZNOkRVJGkFUjK9xPY1jY6nDnYAPidpFmlYfhdJf2xsSEvsKeAp26XRg6tICbaZfRx43Pbztt8GrgG2b3BM9fScpMEA+fvsBsdTF5IOBHYHRrmOD2OIhLrsmQRsKGl9SSuSbqC4vsExLRFJIl2Xe8j2rxodTz3Y/rHttW23kH5Gf7Pd1D0f2/8BnpS0cS76GPBgA0Oqh38D20rql38PP0aT32hV5nrgwLx8IPB/DYylLiR9CvgR8Dnbr9ez7kioy5h8Mf5bwDjSH/4Vth9obFRLbAfgS6Re3NT89elGBxUqOgK4RNJ0YCvg5MaGs2Ryb/sqYAowg/Q/tSkf1yfpUuAeYGNJT0k6FDgV+ISkR4FP5NdNo4NjOgtYFbgl/6/4Xd3ai0cPhhBCCEsueqghhBBCHURCDSGEEOogEmoIIYRQB5FQQwghhDqIhBpCCCHUQSTUEHqQJEs6vfD6KEnH1anusZJG1qOuTtrZJ88Uc3tZeYukNwofXZqaP+u8pO29c1z5cYXdNvGBpPF5JqZpkiZJ2qrO9Z8g6eN1qGfE0jID0dJk+UYHEMIyZh7weUmn2H6h0cGUSOpju73GzQ8FvmH79grr/mV7q/pFtijbX+muugtG2W6TdDBwGunzl3Vh+5h61RV6n+ihhtCz5pM++P/d8hXlPUxJc/P3EZLukHSFpH9IOlXSKEkTJc2Q9P5CNR+XdGfebve8f588B+SkPAfkVwv13i7pT6SHEpTHs3+uf6akX+SyY4Adgd9JOq2WAy4dR14eKWls4XjPlPR3SY8VeqGSdJakByXdROGB7LkH2VqqV9JJuTd5r6RBufz9+fWk3CMsncfBkibknvNMSTt1Evo95IkjJK2iNLfmJKUH++9ROLe/zOdpuqQjcvmw/DObLGmcFj6+b2w+B7tJuqJwXCMk3ZCXd5V0j6Qpkq5UekZ1aR7jhyXdBXy+lnMfelYk1BB63tnAKEkDurDPlqS5UTcnPRVqI9vbkKZ2O6KwXQvwUdK0b79Tmuz6UNIsKMOB4cBhktbP228D/MT2IsOoktYCfgHsQnqi0XBJe9o+AWgj9eJ+UCHO9xeGe8+u4bgGkxL07ix8Cs9ewMb5WA+j42fjrgLca3tLYELeFtKcq2fk4y0+p/qLwLjcg94SmNpJbJ8CrsvLPyE9/nE4sDNwmtJMOYeTHoy/dZ5f8xKl50r/FhhpexhwPnBSWd23kB5ZWJptZ1/gckkDgZ8CH7c9lHSuv5d/jmOAzwI7Af/TSeyhAWLIN4QeZvsVSReRJqZ+o8bdJpWm0ZL0L6A0RdgM0j/4kitsLwAelfQYsAmwK7BFofc7ANgQeAuYaPvxCu0NB8bbfj63eQlpLtPrOomzq0O+1+V4Hyz1MHM7l+Yh6Gck/a2Dfd8CStcRJ7NwaHY7Fs7b+SfS9GqQnmN9fk5419me2kG9l+RE14eFD+/flTRZwVH59crAuqSH4/+uNL+m7RclbQZsRnq0HbmeZ4sN2J4v6a/AZyVdRXoD9EPSm6EPAXfnfVck9ZQ3IT2E/1EApYkSDu8g/tAgkVBDaIzfkJ7/ekGhbD551Ejpv2nxhp55heUFhdcLWPTvuPxZoiZN2XeE7XHFFZJGkKZQq6TSNH+LqxjTymXrisdVbLOWZ6K+XZgppJ1O/p/ZniDpI6TkdbGk02xfVGHTUcA0Uo/5bNLwqoC9bT9S3DD/nMpjFfCA7e06if9y4JukCbAn2X4113eL7f3L2tmqQjuhl4kh3xAawPaLwBWk4diSWcCwvLwHsMJiVL2PpOXyddUNgEdIEyF8PffMkLSROp/Y+z7go5IGSuoD7A/csRjxQJoC7IOSliMN53ZmArBfvj45mEV74LW4F9g7L+9XKpS0HmmO2TGk2Yk6nDouT8X2U9Kw7AdJ5/CInPCQtHXe9Gbga5KWz+Wrkc75GpK2y2UrSNq0QjPjcwyHkZJrKfYdJH0g79tP0kbAw8D6Wni9fH9CrxMJNYTGOR0YWHg9hpTEJgIfpuPeYzWPkBLfX4Cv2X6TdJ31QWCKpJnA7+m8N/cs8GPgdlJvbYrtxZ2662jS0OzfKBv67MC1wKOk4exz6Xoi/w7puuNE0jXaObl8BDBV0v2khHtGtUpsv0H6GR0FnEh6gzM9n8MT82bnkaZwmy5pGvBF228BI4Ff5LKpVLgOnIe0bwR2y9/JQ+wHAZcqzchzL7BJ/jkeDtyUb0p6oktnJPSImG0mhLBUkdQPeMO2Je0H7G97j0bHFZZ+cQ01hLC0GQaclYdnXwYOaWw4YVkRPdQQQgihDuIaagghhFAHkVBDCCGEOoiEGkIIIdRBJNQQQgihDiKhhhBCCHXw/wHT0JYKJeZAMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "founders[1:].head(10).sort_values().plot.barh() # Plotting while excluding the \"Unknown Founders\"\n",
    "plt.xlabel(\"Number of Fundings Received\")\n",
    "plt.ylabel(\"Name of Founder\")\n",
    "plt.title(\"Number of Known Fundings per Founder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c6b942fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>540000000.00</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2019</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>South Africa’s Naspers Ventures, the CPP Inves...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>660000000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Zomato is a food delivery and restaurant disco...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>Kora, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>Unacademy</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Unacademy is an online learning platform provi...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2015</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Dragoneer Investment Group, Tiger Global Manag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>200000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Owl Ventures, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>52000000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Zomato is a food delivery and restaurant disco...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>Kora, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>Byju’s</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>An Indian educational technology and online tu...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Owl Ventures, Tiger Global Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Unacademy</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>150000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Unacademy is an Indian online education techno...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2015</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Nexus Venture Partners, Sequoia Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>62000000.00</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Get online food delivery from restaurants near...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>MacRitchie Investments, Baillie Gifford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>100000000.00</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Get online food delivery from restaurants near...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>MacRitchie Investments, Baillie Gifford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>122000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>DST Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>Vedantu</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series D</td>\n",
       "      <td>100000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Onine learning platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>Coatue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>Uable</td>\n",
       "      <td>Education</td>\n",
       "      <td>Seed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Online life skills development platform for ch...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>3one4 Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>BOND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>Vedantu</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series C</td>\n",
       "      <td>6800000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Onine learning platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>KB Global Platform Firm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>Vedantu</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series C</td>\n",
       "      <td>12500000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Onine learning platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>Legend Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Online food delivery and restaurant discovery ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>Pacific Horizon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series C</td>\n",
       "      <td>75000000.00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Digital payments platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Ribbit Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>Unacademy</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series E</td>\n",
       "      <td>110000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Platform that offers online education in India</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Facebook, General Atlantic, Sequoia India, Nex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>Vedantu</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series C</td>\n",
       "      <td>24000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Onine learning platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>GGV Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>200000000.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>General Atlantic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>100000000.00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Digital payments platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Coatue Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>Rheo</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Online e-sports streaming and community platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Lightspeed Venture Partners, Sequoia Surge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>Zomato</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>150000000.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Online food delivery and restaurant discovery ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Deepinder Goyal</td>\n",
       "      <td>Ant Financial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>200000000.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2020</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Tiger Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>Uable</td>\n",
       "      <td>Education</td>\n",
       "      <td>Seed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Uable offers role based programmes to empower ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>Chiratae Ventures, JAFCO Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>Uable</td>\n",
       "      <td>Education</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>35000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Uable are on a bold mission to redefine the fu...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Saurabh Saxena</td>\n",
       "      <td>JAFCO Asia, Chiratae Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>20000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>The vision to make financial inclusion a reali...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>IIFL Wealth, Asset Management, Northern Arc Ca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>Unacademy</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Unacademy aims to build the world's largest on...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Bhavin Turakhia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series E</td>\n",
       "      <td>370000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe launched India’s first UPI interopera...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Tiger Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series D</td>\n",
       "      <td>100000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Amplo, Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>InnoVen Capital, Steadview Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>InnoVen Capital, Steadview Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>Unacademy</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Unacademy is an online learning platform provi...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Tiger Global, Dragoneer Investment Group, Stea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2092</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>8200000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe develops a QR code-based payment app ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Steadview Capital, Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>350000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Tiga Investments, Baron Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2310</th>\n",
       "      <td>Byju’s</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Blackstone Group, ADQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series F</td>\n",
       "      <td>460000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>BlackRock, Owl Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe is the definitive platform for financ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>Amplo, Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>150000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU'S is an educational technology company th...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Tiga Investments, Baron Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2596</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>300000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>BYJU’S is India's largest ed-tech company and ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>Oxshott Capital Partners, XN Exponent, Edelweiss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2676</th>\n",
       "      <td>Bharatpe</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BharatPe launched India’s first UPI interopera...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Ashneer Grover</td>\n",
       "      <td>MAS Financial Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company Name              Sector Funding Stage       Amount  \\\n",
       "541        Byju's           Education   Undisclosed 540000000.00   \n",
       "629        Zomato    Food & Nutrition   Undisclosed 660000000.00   \n",
       "730     Unacademy           Education   Undisclosed         0.00   \n",
       "738        Byju's           Education   Undisclosed 200000000.00   \n",
       "859        Zomato    Food & Nutrition   Undisclosed  52000000.00   \n",
       "938        Byju’s           Education   Undisclosed 500000000.00   \n",
       "956     Unacademy           Education   Undisclosed 150000000.00   \n",
       "960        Zomato    Food & Nutrition   Undisclosed  62000000.00   \n",
       "978        Zomato    Food & Nutrition   Undisclosed 100000000.00   \n",
       "1104       Byju's           Education   Undisclosed 122000000.00   \n",
       "1178      Vedantu           Education      Series D 100000000.00   \n",
       "1187        Uable           Education          Seed         0.00   \n",
       "1228       Byju's           Education   Undisclosed         0.00   \n",
       "1366      Vedantu           Education      Series C   6800000.00   \n",
       "1402      Vedantu           Education      Series C  12500000.00   \n",
       "1411       Zomato    Food & Nutrition   Undisclosed   5000000.00   \n",
       "1497     Bharatpe             Fintech      Series C  75000000.00   \n",
       "1507    Unacademy           Education      Series E 110000000.00   \n",
       "1529      Vedantu           Education      Series C  24000000.00   \n",
       "1544       Byju's           Education   Undisclosed 200000000.00   \n",
       "1570     Bharatpe             Fintech   Undisclosed 100000000.00   \n",
       "1634         Rheo       Entertainment   Undisclosed   2000000.00   \n",
       "1643       Zomato    Food & Nutrition   Undisclosed 150000000.00   \n",
       "1644       Byju's           Education   Undisclosed 200000000.00   \n",
       "1685        Uable           Education          Seed         0.00   \n",
       "1744        Uable           Education  Pre-series A  35000000.00   \n",
       "1803     Bharatpe             Fintech          Debt  20000000.00   \n",
       "1830    Unacademy           Education   Undisclosed         0.00   \n",
       "1845     Bharatpe             Fintech      Series E 370000000.00   \n",
       "1984     Bharatpe             Fintech      Series D 100000000.00   \n",
       "2046     Bharatpe             Fintech          Debt   6000000.00   \n",
       "2052     Bharatpe             Fintech          Debt  10000000.00   \n",
       "2071    Unacademy           Education   Undisclosed  50000000.00   \n",
       "2092     Bharatpe             Fintech          Debt   8200000.00   \n",
       "2283       Byju's           Education   Undisclosed 350000000.00   \n",
       "2310       Byju’s           Education   Undisclosed  50000000.00   \n",
       "2381       Byju's           Education      Series F 460000000.00   \n",
       "2492     Bharatpe             Fintech          Debt   6000000.00   \n",
       "2507       Byju's           Education   Undisclosed 150000000.00   \n",
       "2596       Byju's           Education   Undisclosed 300000000.00   \n",
       "2676     Bharatpe  Financial services   Undisclosed  10000000.00   \n",
       "\n",
       "          Headquarters                                        Description  \\\n",
       "541   Unknown Location                   Provides online learning classes   \n",
       "629            Gurgaon  Zomato is a food delivery and restaurant disco...   \n",
       "730          Bangalore  Unacademy is an online learning platform provi...   \n",
       "738          Bangalore  BYJU'S is an educational technology company th...   \n",
       "859            Gurgaon  Zomato is a food delivery and restaurant disco...   \n",
       "938          Bangalore  An Indian educational technology and online tu...   \n",
       "956          Bangalore  Unacademy is an Indian online education techno...   \n",
       "960            Haryana  Get online food delivery from restaurants near...   \n",
       "978            Haryana  Get online food delivery from restaurants near...   \n",
       "1104         Bangalore                   Provides online learning classes   \n",
       "1178         Bangalore                            Onine learning platform   \n",
       "1187         Bangalore  Online life skills development platform for ch...   \n",
       "1228         Bangalore                   Provides online learning classes   \n",
       "1366         Bangalore                            Onine learning platform   \n",
       "1402         Bangalore                            Onine learning platform   \n",
       "1411           Gurgaon  Online food delivery and restaurant discovery ...   \n",
       "1497             Delhi                          Digital payments platform   \n",
       "1507         Bangalore     Platform that offers online education in India   \n",
       "1529         Bangalore                            Onine learning platform   \n",
       "1544           Unknown                   Provides online learning classes   \n",
       "1570             Delhi                          Digital payments platform   \n",
       "1634           Unknown   Online e-sports streaming and community platform   \n",
       "1643           Unknown  Online food delivery and restaurant discovery ...   \n",
       "1644           Unknown                   Provides online learning classes   \n",
       "1685         Bangalore  Uable offers role based programmes to empower ...   \n",
       "1744         Bangalore  Uable are on a bold mission to redefine the fu...   \n",
       "1803         New Delhi  The vision to make financial inclusion a reali...   \n",
       "1830         Bangalore  Unacademy aims to build the world's largest on...   \n",
       "1845         New Delhi  BharatPe launched India’s first UPI interopera...   \n",
       "1984         New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2046         New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2052         New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2071         Bangalore  Unacademy is an online learning platform provi...   \n",
       "2092         New Delhi  BharatPe develops a QR code-based payment app ...   \n",
       "2283         Bangalore  BYJU'S is an educational technology company th...   \n",
       "2310         Bangalore  BYJU'S is an educational technology company th...   \n",
       "2381         Bangalore  BYJU'S is an educational technology company th...   \n",
       "2492         New Delhi  BharatPe is the definitive platform for financ...   \n",
       "2507         Bangalore  BYJU'S is an educational technology company th...   \n",
       "2596         Bangalore  BYJU’S is India's largest ed-tech company and ...   \n",
       "2676         New Delhi  BharatPe launched India’s first UPI interopera...   \n",
       "\n",
       "     Funding Year Year Founded         Founders  \\\n",
       "541          2019         2011  Byju Raveendran   \n",
       "629          2020         2008  Deepinder Goyal   \n",
       "730          2020         2015    Gaurav Munjal   \n",
       "738          2020         2011  Byju Raveendran   \n",
       "859          2020         2008  Deepinder Goyal   \n",
       "938          2020         2011  Byju Raveendran   \n",
       "956          2020         2015    Gaurav Munjal   \n",
       "960          2020         2008  Deepinder Goyal   \n",
       "978          2020         2008  Deepinder Goyal   \n",
       "1104         2020         2011  Byju Raveendran   \n",
       "1178         2020         2011   Saurabh Saxena   \n",
       "1187         2020          nan   Saurabh Saxena   \n",
       "1228         2020         2011  Byju Raveendran   \n",
       "1366         2020          nan   Saurabh Saxena   \n",
       "1402         2020          nan   Saurabh Saxena   \n",
       "1411         2020          nan  Deepinder Goyal   \n",
       "1497         2020         2018   Ashneer Grover   \n",
       "1507         2020         2010    Gaurav Munjal   \n",
       "1529         2020          nan   Saurabh Saxena   \n",
       "1544         2020         2011  Byju Raveendran   \n",
       "1570         2020         2018   Ashneer Grover   \n",
       "1634         2020          nan    Gaurav Munjal   \n",
       "1643         2020          nan  Deepinder Goyal   \n",
       "1644         2020         2011  Byju Raveendran   \n",
       "1685         2021         2020   Saurabh Saxena   \n",
       "1744         2021         2020   Saurabh Saxena   \n",
       "1803         2021         2018   Ashneer Grover   \n",
       "1830         2021         2015    Gaurav Munjal   \n",
       "1845         2021         2018   Ashneer Grover   \n",
       "1984         2021         2018   Ashneer Grover   \n",
       "2046         2021         2018   Ashneer Grover   \n",
       "2052         2021         2018   Ashneer Grover   \n",
       "2071         2021         2015    Gaurav Munjal   \n",
       "2092         2021         2018   Ashneer Grover   \n",
       "2283         2021         2011  Byju Raveendran   \n",
       "2310         2021         2011  Byju Raveendran   \n",
       "2381         2021         2011  Byju Raveendran   \n",
       "2492         2021         2018   Ashneer Grover   \n",
       "2507         2021         2011  Byju Raveendran   \n",
       "2596         2021         2011  Byju Raveendran   \n",
       "2676         2021         2018   Ashneer Grover   \n",
       "\n",
       "                                              Investors  \n",
       "541   South Africa’s Naspers Ventures, the CPP Inves...  \n",
       "629                       Kora, Tiger Global Management  \n",
       "730   Dragoneer Investment Group, Tiger Global Manag...  \n",
       "738               Owl Ventures, Tiger Global Management  \n",
       "859                       Kora, Tiger Global Management  \n",
       "938               Owl Ventures, Tiger Global Management  \n",
       "956             Nexus Venture Partners, Sequoia Capital  \n",
       "960             MacRitchie Investments, Baillie Gifford  \n",
       "978             MacRitchie Investments, Baillie Gifford  \n",
       "1104                                         DST Global  \n",
       "1178                                             Coatue  \n",
       "1187                                      3one4 Capital  \n",
       "1228                                               BOND  \n",
       "1366                            KB Global Platform Firm  \n",
       "1402                                     Legend Capital  \n",
       "1411                                    Pacific Horizon  \n",
       "1497                                     Ribbit Capital  \n",
       "1507  Facebook, General Atlantic, Sequoia India, Nex...  \n",
       "1529                                        GGV Capital  \n",
       "1544                                   General Atlantic  \n",
       "1570                                  Coatue Management  \n",
       "1634         Lightspeed Venture Partners, Sequoia Surge  \n",
       "1643                                      Ant Financial  \n",
       "1644                                       Tiger Global  \n",
       "1685                      Chiratae Ventures, JAFCO Asia  \n",
       "1744                      JAFCO Asia, Chiratae Ventures  \n",
       "1803  IIFL Wealth, Asset Management, Northern Arc Ca...  \n",
       "1830                                    Bhavin Turakhia  \n",
       "1845                                       Tiger Global  \n",
       "1984                            Amplo, Insight Partners  \n",
       "2046                 InnoVen Capital, Steadview Capital  \n",
       "2052                 InnoVen Capital, Steadview Capital  \n",
       "2071  Tiger Global, Dragoneer Investment Group, Stea...  \n",
       "2092                Steadview Capital, Insight Partners  \n",
       "2283                    Tiga Investments, Baron Capital  \n",
       "2310                              Blackstone Group, ADQ  \n",
       "2381                            BlackRock, Owl Ventures  \n",
       "2492                            Amplo, Insight Partners  \n",
       "2507                    Tiga Investments, Baron Capital  \n",
       "2596   Oxshott Capital Partners, XN Exponent, Edelweiss  \n",
       "2676                             MAS Financial Services  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look at which \"startups\" were founded earlier than 2000\n",
    "complete_set.loc[(complete_set[\"Founders\"].str.split(',').str[0] == \"Byju Raveendran\") |\n",
    "                (complete_set[\"Founders\"].str.split(',').str[0] == \"Ashneer Grover\") |\n",
    "                (complete_set[\"Founders\"].str.split(',').str[0] == \"Saurabh Saxena\") |\n",
    "                (complete_set[\"Founders\"].str.split(',').str[0] == \"Gaurav Munjal\") |\n",
    "                 (complete_set[\"Founders\"].str.split(',').str[0] == \"Deepinder Goyal\") |\n",
    "                (complete_set[\"Founders\"].str.split(',').str[0] == \"bhavish Aggarwal\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33411f09",
   "metadata": {},
   "source": [
    "Maybe beside the point, but we note here that Byju Raveendran's startups were involved in most deals over the period (12). This is followed by Ashneer Grover (10) with Saurabh Saxena (7) not far behind."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "193cfa9a",
   "metadata": {},
   "source": [
    "#### 3.8.10 Investors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "631532e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Undisclosed                  641\n",
       "Venture Catalysts             52\n",
       "Inflection Point Ventures     42\n",
       "Sequoia Capital India         25\n",
       "Tiger Global                  24\n",
       "                            ... \n",
       "Bharti Airtel                  1\n",
       "IDG Ventures                   1\n",
       "Angel Investors                1\n",
       "Kalyan Krishnamurthy           1\n",
       "Equanimity Investments         1\n",
       "Name: Investors, Length: 1246, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set[\"Investors\"].apply(str)\n",
    "complete_set[\"Investors\"] = complete_set[\"Investors\"].str.split(',').str[0]\n",
    "complete_set[\"Investors\"].fillna(\"Undisclosed\", inplace = True)\n",
    "complete_set[\"Investors\"].replace(\"  \",\" \", inplace = True)\n",
    "complete_set[\"Investors\"].replace(\"$Undisclosed\",\"Undisclosed\", inplace = True)\n",
    "investors = (complete_set.loc[:,\"Investors\"]).value_counts()\n",
    "investors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ecb1d338",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Known Investments per Investor')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "investors[1:].head(10).sort_values().plot.barh() # Filtering the top 10 investors by investment while excluding the \"Undisclosed\"\n",
    "plt.xlabel(\"Number of Investments Made\")\n",
    "plt.ylabel(\"Name of Investor\")\n",
    "plt.title(\"Number of Known Investments per Investor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ddb9ef",
   "metadata": {},
   "source": [
    "Despite filtering out the 627 unknown investors (data not available in the dataset), we note a total of about 1,245 different investors over the period.\n",
    "\n",
    "We also note that Venture Catalysts (52) and Inflection Point Ventures (42) made the most investments, by number of transactions, over the period. They are followed by Sequoia Capital India (25) and Tiger Global (24).\n",
    "\n",
    "Next up, we take a look at the amounts involved in the deals by the various investors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bf18d42c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investors</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Silver Lake</th>\n",
       "      <td>1</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>70000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salesforce Ventures</th>\n",
       "      <td>2</td>\n",
       "      <td>3006700000.00</td>\n",
       "      <td>1503350000.00</td>\n",
       "      <td>1503350000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiger Global</th>\n",
       "      <td>24</td>\n",
       "      <td>2356000000.00</td>\n",
       "      <td>98166666.67</td>\n",
       "      <td>75500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facebook</th>\n",
       "      <td>2</td>\n",
       "      <td>2310000000.00</td>\n",
       "      <td>1155000000.00</td>\n",
       "      <td>1155000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Atlantic</th>\n",
       "      <td>7</td>\n",
       "      <td>1647100000.00</td>\n",
       "      <td>235300000.00</td>\n",
       "      <td>200000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katrina Kaif</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kapil Dev</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Krishnamurthy</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kae Capital</th>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legatum Capital</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1245 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count            sum           mean         median\n",
       "Investors                                                               \n",
       "Silver Lake               1 70000000000.00 70000000000.00 70000000000.00\n",
       "Salesforce Ventures       2  3006700000.00  1503350000.00  1503350000.00\n",
       "Tiger Global             24  2356000000.00    98166666.67    75500000.00\n",
       "Facebook                  2  2310000000.00  1155000000.00  1155000000.00\n",
       "General Atlantic          7  1647100000.00   235300000.00   200000000.00\n",
       "...                     ...            ...            ...            ...\n",
       "Katrina Kaif              1           0.00           0.00           0.00\n",
       "Kapil Dev                 1           0.00           0.00           0.00\n",
       "Kalyan Krishnamurthy      1           0.00           0.00           0.00\n",
       "Kae Capital               2           0.00           0.00           0.00\n",
       "Legatum Capital           1           0.00           0.00           0.00\n",
       "\n",
       "[1245 rows x 4 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investors_set = (complete_set.groupby(by = \"Investors\").Amount.agg([\"count\",\"sum\", \"mean\", \"median\"]).sort_values(by = \"sum\", ascending = False))[1:]\n",
    "investors_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fc58c82c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Value of the Total Investments by Investor')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = (investors_set[\"sum\"])[:15], y = (investors_set.index)[:15])\n",
    "plt.xlabel(\"Total Value of Investments\")\n",
    "plt.ylabel(\"Investor\")\n",
    "plt.title(\"Value of the Total Investments by Investor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9611e18",
   "metadata": {},
   "source": [
    "Even with the foregoing, we note that Silver Lake's single deal of USD 70bn investment tops the list of investments by value. They are followed in a distance by Salesforce Ventures with 2 transactions summing up to USD 3bn, Tiger Global (24 transactions) with USD 2.356bn, Facebook (2 transactions) with USD 2.31bn, and then General Atlantic (7 transactions) with USD 1.65bn. \n",
    "\n",
    "This creates the impression that number of deals does not necessarily imply high value of investments since only one of the top four investors involved in deals featured in the top 5 by value of total investments. Shall we attempt confirm? Yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bb0bd6cd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Value of Investment Deals by Investor')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot to visualize the relationship between count of investment deals and total value of investments\n",
    "plt.figure(figsize = (20,12))\n",
    "sns.regplot(y = (investors_set[\"sum\"]), x = (investors_set[\"count\"]))\n",
    "#sns.lineplot(y = (investors_set[\"sum\"]), x = (investors_set[\"count\"]))\n",
    "plt.xlabel(\"Number of Investment Deals\")\n",
    "plt.ylabel(\"Total Value\")\n",
    "plt.title(\"Total Value of Investment Deals by Investor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6b406e",
   "metadata": {},
   "source": [
    "From the plot, it is safe to conclude that there is no relationship between the number of investment deals and the monetary value of investments. It is however noteworthy that aside Tiger Global (24), investors with less than 10 transactions seemed to have made more investments by value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dfe38802",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investors</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Silver Lake</th>\n",
       "      <td>1</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>70000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salesforce Ventures</th>\n",
       "      <td>2</td>\n",
       "      <td>3006700000.00</td>\n",
       "      <td>1503350000.00</td>\n",
       "      <td>1503350000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facebook</th>\n",
       "      <td>2</td>\n",
       "      <td>2310000000.00</td>\n",
       "      <td>1155000000.00</td>\n",
       "      <td>1155000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canaan Valley Capital</th>\n",
       "      <td>1</td>\n",
       "      <td>1000000000.00</td>\n",
       "      <td>1000000000.00</td>\n",
       "      <td>1000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carmignac</th>\n",
       "      <td>1</td>\n",
       "      <td>800000000.00</td>\n",
       "      <td>800000000.00</td>\n",
       "      <td>800000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MyPreferred Transformation</th>\n",
       "      <td>1</td>\n",
       "      <td>693000000.00</td>\n",
       "      <td>693000000.00</td>\n",
       "      <td>693000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoftBank Vision Fund</th>\n",
       "      <td>1</td>\n",
       "      <td>660000000.00</td>\n",
       "      <td>660000000.00</td>\n",
       "      <td>660000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa’s Naspers Ventures</th>\n",
       "      <td>1</td>\n",
       "      <td>540000000.00</td>\n",
       "      <td>540000000.00</td>\n",
       "      <td>540000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twitter Ventures</th>\n",
       "      <td>1</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>500000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marquee international institutional investors</th>\n",
       "      <td>1</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>500000000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               count            sum  \\\n",
       "Investors                                                             \n",
       "Silver Lake                                        1 70000000000.00   \n",
       "Salesforce Ventures                                2  3006700000.00   \n",
       "Facebook                                           2  2310000000.00   \n",
       "Canaan Valley Capital                              1  1000000000.00   \n",
       "Carmignac                                          1   800000000.00   \n",
       "MyPreferred Transformation                         1   693000000.00   \n",
       "SoftBank Vision Fund                               1   660000000.00   \n",
       "South Africa’s Naspers Ventures                    1   540000000.00   \n",
       "Twitter Ventures                                   1   500000000.00   \n",
       "Marquee international institutional investors      1   500000000.00   \n",
       "\n",
       "                                                        mean         median  \n",
       "Investors                                                                    \n",
       "Silver Lake                                   70000000000.00 70000000000.00  \n",
       "Salesforce Ventures                            1503350000.00  1503350000.00  \n",
       "Facebook                                       1155000000.00  1155000000.00  \n",
       "Canaan Valley Capital                          1000000000.00  1000000000.00  \n",
       "Carmignac                                       800000000.00   800000000.00  \n",
       "MyPreferred Transformation                      693000000.00   693000000.00  \n",
       "SoftBank Vision Fund                            660000000.00   660000000.00  \n",
       "South Africa’s Naspers Ventures                 540000000.00   540000000.00  \n",
       "Twitter Ventures                                500000000.00   500000000.00  \n",
       "Marquee international institutional investors   500000000.00   500000000.00  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the monetary value of the average investment deal by an investor\n",
    "investors_set = investors_set.sort_values(by = \"median\", ascending = False)\n",
    "investors_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "59525d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Value of Investments by Investor')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Due to it's outlying effect, Silver Lake will be excluded from the visualization to have a better look at the others\n",
    "sns.barplot(x = (investors_set[\"mean\"])[1:16], y = (investors_set.index)[1:16])\n",
    "plt.xlabel(\"Average Value of Investments\")\n",
    "plt.ylabel(\"Investor\")\n",
    "plt.title(\"Average Value of Investments by Investor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddff0d8",
   "metadata": {},
   "source": [
    "Here, we note that Salesforce Ventures (USD 1.5bn per deal) make the best investors after Silver Lake (USD 70bn for a single deal). Facebook (USD 1.15bn per deal), Canaan Valley Capital (USD 1bn for a single deal), and Carmignac (USD 800m for a single deal) also follow in that order.\n",
    "\n",
    "We note that the investors who made the biggest investments over the period were involved in less than 5 deals over the period. This implies that whichever startup intends to get funding from any of these investors have to get their work right. Given that these investors have invested in a variety of sectors, it is difficult to conclude that a startup is likely to land a funding deal with these top investors by investing in a particular sector. \n",
    "\n",
    "We must however not rule out the fact that the education sector had the most funding deals over the period and also tops this list (with the tourism & hospitality industry) as the most favoured sector (on average) by the top investors. We can therefore advice that, other things being equal, a startup that intends to get higher than average funding can consider the education sector as its first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "370c8242",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Education                             2\n",
      "Tourism & Hospitality                 2\n",
      "Retail                                1\n",
      "Tech                                  1\n",
      "Multinational conglomerate company    1\n",
      "Food & Nutrition                      1\n",
      "Social media & networking             1\n",
      "Software                              1\n",
      "Travel & Transport                    1\n",
      "Innovation management                 1\n",
      "Name: Sector, dtype: int64 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Byju's</td>\n",
       "      <td>Education</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>540000000.00</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Provides online learning classes</td>\n",
       "      <td>2019</td>\n",
       "      <td>2011</td>\n",
       "      <td>Byju Raveendran</td>\n",
       "      <td>South Africa’s Naspers Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Oyo</td>\n",
       "      <td>Tourism &amp; Hospitality</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>693000000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Provides rooms for comfortable stay</td>\n",
       "      <td>2019</td>\n",
       "      <td>2013</td>\n",
       "      <td>Ritesh Agarwal</td>\n",
       "      <td>MyPreferred Transformation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Reliance retail ventures ltd</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance Retail offers online shopping and a c...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mukesh Ambani</td>\n",
       "      <td>Silver Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>Snowflake</td>\n",
       "      <td>Tech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>3000000000.00</td>\n",
       "      <td>California</td>\n",
       "      <td>Snowflake is a cloud data platform that provid...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2012</td>\n",
       "      <td>Benoit Dageville</td>\n",
       "      <td>Salesforce Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>Reliance</td>\n",
       "      <td>Multinational conglomerate company</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Reliance owns businesses across India engaged ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>1973</td>\n",
       "      <td>Dhirubhai Ambani</td>\n",
       "      <td>Facebook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>Unacademy</td>\n",
       "      <td>Education</td>\n",
       "      <td>Series E</td>\n",
       "      <td>110000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Platform that offers online education in India</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Gaurav Munjal</td>\n",
       "      <td>Facebook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>Swiggy</td>\n",
       "      <td>Food &amp; Nutrition</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>800000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Swiggy is an on-demand food delivery platform ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2014</td>\n",
       "      <td>Nandan Reddy</td>\n",
       "      <td>Carmignac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>Sharechat</td>\n",
       "      <td>Social media &amp; networking</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>ShareChat is a social networking and regional ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015</td>\n",
       "      <td>Ankush Sachdeva</td>\n",
       "      <td>Twitter Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>Hashnode</td>\n",
       "      <td>Software</td>\n",
       "      <td>Series A</td>\n",
       "      <td>6700000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>The easiest way to start a blog on personal do...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2016</td>\n",
       "      <td>Syed Fazle Rahman</td>\n",
       "      <td>Salesforce Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>Ola</td>\n",
       "      <td>Travel &amp; Transport</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>500000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Ola is India’s largest mobility platform and o...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2010</td>\n",
       "      <td>Bhavish Aggarwal</td>\n",
       "      <td>Marquee international institutional investors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>Verse innovation</td>\n",
       "      <td>Innovation management</td>\n",
       "      <td>Series H</td>\n",
       "      <td>1000000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Owner of Dailyhunt App and I-Pay</td>\n",
       "      <td>2021</td>\n",
       "      <td>2007</td>\n",
       "      <td>Virendra Gupta</td>\n",
       "      <td>Canaan Valley Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2177</th>\n",
       "      <td>Oyo</td>\n",
       "      <td>Tourism &amp; Hospitality</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>660000000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>OYO is a global platform that empowers entrepr...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2013</td>\n",
       "      <td>Ritesh Agarwal</td>\n",
       "      <td>SoftBank Vision Fund</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Company Name                              Sector  \\\n",
       "541                         Byju's                           Education   \n",
       "610                            Oyo               Tourism & Hospitality   \n",
       "892   Reliance retail ventures ltd                              Retail   \n",
       "929                      Snowflake                                Tech   \n",
       "940                       Reliance  Multinational conglomerate company   \n",
       "1507                     Unacademy                           Education   \n",
       "1717                        Swiggy                    Food & Nutrition   \n",
       "1734                     Sharechat           Social media & networking   \n",
       "1801                      Hashnode                            Software   \n",
       "1912                           Ola                  Travel & Transport   \n",
       "1991              Verse innovation               Innovation management   \n",
       "2177                           Oyo               Tourism & Hospitality   \n",
       "\n",
       "     Funding Stage         Amount      Headquarters  \\\n",
       "541    Undisclosed   540000000.00  Unknown Location   \n",
       "610    Undisclosed   693000000.00           Gurgaon   \n",
       "892    Undisclosed 70000000000.00            Mumbai   \n",
       "929    Undisclosed  3000000000.00        California   \n",
       "940    Undisclosed  2200000000.00            Mumbai   \n",
       "1507      Series E   110000000.00         Bangalore   \n",
       "1717   Undisclosed   800000000.00         Bangalore   \n",
       "1734   Undisclosed   500000000.00         Bangalore   \n",
       "1801      Series A     6700000.00         Bangalore   \n",
       "1912   Undisclosed   500000000.00         Bangalore   \n",
       "1991      Series H  1000000000.00         Bangalore   \n",
       "2177   Undisclosed   660000000.00           Gurgaon   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "541                    Provides online learning classes         2019   \n",
       "610                 Provides rooms for comfortable stay         2019   \n",
       "892   Reliance Retail offers online shopping and a c...         2020   \n",
       "929   Snowflake is a cloud data platform that provid...         2020   \n",
       "940   Reliance owns businesses across India engaged ...         2020   \n",
       "1507     Platform that offers online education in India         2020   \n",
       "1717  Swiggy is an on-demand food delivery platform ...         2021   \n",
       "1734  ShareChat is a social networking and regional ...         2021   \n",
       "1801  The easiest way to start a blog on personal do...         2021   \n",
       "1912  Ola is India’s largest mobility platform and o...         2021   \n",
       "1991                   Owner of Dailyhunt App and I-Pay         2021   \n",
       "2177  OYO is a global platform that empowers entrepr...         2021   \n",
       "\n",
       "     Year Founded           Founders  \\\n",
       "541          2011    Byju Raveendran   \n",
       "610          2013     Ritesh Agarwal   \n",
       "892          2006      Mukesh Ambani   \n",
       "929          2012   Benoit Dageville   \n",
       "940          1973   Dhirubhai Ambani   \n",
       "1507         2010      Gaurav Munjal   \n",
       "1717         2014       Nandan Reddy   \n",
       "1734         2015    Ankush Sachdeva   \n",
       "1801         2016  Syed Fazle Rahman   \n",
       "1912         2010   Bhavish Aggarwal   \n",
       "1991         2007     Virendra Gupta   \n",
       "2177         2013     Ritesh Agarwal   \n",
       "\n",
       "                                          Investors  \n",
       "541                 South Africa’s Naspers Ventures  \n",
       "610                      MyPreferred Transformation  \n",
       "892                                     Silver Lake  \n",
       "929                             Salesforce Ventures  \n",
       "940                                        Facebook  \n",
       "1507                                       Facebook  \n",
       "1717                                      Carmignac  \n",
       "1734                               Twitter Ventures  \n",
       "1801                            Salesforce Ventures  \n",
       "1912  Marquee international institutional investors  \n",
       "1991                          Canaan Valley Capital  \n",
       "2177                           SoftBank Vision Fund  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "which_sectors = complete_set.loc[(complete_set[\"Investors\"] == \"Silver Lake\") |\n",
    "                 (complete_set[\"Investors\"] == \"Salesforce Ventures\") |\n",
    "                 (complete_set[\"Investors\"] == \"Facebook\") |\n",
    "                 (complete_set[\"Investors\"] == \"Canaan Valley Capital\") |\n",
    "                 (complete_set[\"Investors\"] == \"Carmignac\") |\n",
    "                 (complete_set[\"Investors\"] == \"MyPreferred Transformation\") |\n",
    "                 (complete_set[\"Investors\"] == \"SoftBank Vision Fund\") |\n",
    "                 (complete_set[\"Investors\"] == \"South Africa’s Naspers Ventures\") |\n",
    "                 (complete_set[\"Investors\"] == \"Twitter Ventures\") |\n",
    "                 (complete_set[\"Investors\"] == \"Marquee international institutional investors\")]\n",
    "print(which_sectors[\"Sector\"].value_counts(), \"\\n\")\n",
    "which_sectors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6eef1da",
   "metadata": {},
   "source": [
    "# 4.0 More Analyses & Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9c1b1ad3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thecollegefever</td>\n",
       "      <td>Advertising, marketing &amp; sales</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy cow dairy</td>\n",
       "      <td>Agriculture &amp; agritech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>584000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Myloancare</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Series A</td>\n",
       "      <td>949000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Payme india</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eunimart</td>\n",
       "      <td>E-commerce</td>\n",
       "      <td>Seed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Eunimart is a one stop solution for merchants ...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>Werize</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Series A</td>\n",
       "      <td>8000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019</td>\n",
       "      <td>Vishal Chopra</td>\n",
       "      <td>3one4 Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2840000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>CESC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2855</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Debt</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>BlackSoil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Debt</td>\n",
       "      <td>408000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>Equanimity Investments</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2858 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Company Name                          Sector Funding Stage  \\\n",
       "0     Thecollegefever  Advertising, marketing & sales          Seed   \n",
       "1     Happy cow dairy          Agriculture & agritech          Seed   \n",
       "2          Myloancare              Financial Services      Series A   \n",
       "3         Payme india              Financial services         Angel   \n",
       "4            Eunimart                      E-commerce          Seed   \n",
       "...               ...                             ...           ...   \n",
       "2853           Werize              Financial services      Series A   \n",
       "2854       Peel works                            Saas      Series D   \n",
       "2855       Peel works                            Saas      Series D   \n",
       "2856       Peel works                            Saas          Debt   \n",
       "2857       Peel works                            Saas          Debt   \n",
       "\n",
       "         Amount Headquarters  \\\n",
       "0     250000.00    Bangalore   \n",
       "1     584000.00       Mumbai   \n",
       "2     949000.00      Gurgaon   \n",
       "3    2000000.00        Noida   \n",
       "4          0.00    Hyderabad   \n",
       "...         ...          ...   \n",
       "2853 8000000.00    Bangalore   \n",
       "2854 2840000.00       Mumbai   \n",
       "2855 2000000.00       Mumbai   \n",
       "2856 1000000.00       Mumbai   \n",
       "2857  408000.00       Mumbai   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "0     TheCollegeFever is a hub for fun, fiesta and f...         2018   \n",
       "1     A startup which aggregates milk from dairy far...         2018   \n",
       "2             Leading Online Loans Marketplace in India         2018   \n",
       "3     PayMe India is an innovative FinTech organizat...         2018   \n",
       "4     Eunimart is a one stop solution for merchants ...         2018   \n",
       "...                                                 ...          ...   \n",
       "2853  India’s first socially distributed full stack ...         2021   \n",
       "2854                                            Unknown         2020   \n",
       "2855                                            Unknown         2020   \n",
       "2856                                            Unknown         2020   \n",
       "2857                                            Unknown         2020   \n",
       "\n",
       "     Year Founded         Founders               Investors  \n",
       "0             nan  Unknown Founder             Undisclosed  \n",
       "1             nan  Unknown Founder             Undisclosed  \n",
       "2             nan  Unknown Founder             Undisclosed  \n",
       "3             nan  Unknown Founder             Undisclosed  \n",
       "4             nan  Unknown Founder             Undisclosed  \n",
       "...           ...              ...                     ...  \n",
       "2853         2019    Vishal Chopra           3one4 Capital  \n",
       "2854         2010   Sachin Chhabra                    CESC  \n",
       "2855         2010   Sachin Chhabra             Undisclosed  \n",
       "2856         2010   Sachin Chhabra               BlackSoil  \n",
       "2857         2010   Sachin Chhabra  Equanimity Investments  \n",
       "\n",
       "[2858 rows x 10 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f0db04d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thecollegefever</td>\n",
       "      <td>Advertising, marketing &amp; sales</td>\n",
       "      <td>Seed</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>TheCollegeFever is a hub for fun, fiesta and f...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Happy cow dairy</td>\n",
       "      <td>Agriculture &amp; agritech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>584000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A startup which aggregates milk from dairy far...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Myloancare</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Series A</td>\n",
       "      <td>949000.00</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Leading Online Loans Marketplace in India</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Payme india</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Angel</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Noida</td>\n",
       "      <td>PayMe India is an innovative FinTech organizat...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hasura</td>\n",
       "      <td>Cloud computing</td>\n",
       "      <td>Seed</td>\n",
       "      <td>1600000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Hasura is a platform that allows developers to...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>Werize</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>Series A</td>\n",
       "      <td>8000000.00</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India’s first socially distributed full stack ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019</td>\n",
       "      <td>Vishal Chopra</td>\n",
       "      <td>3one4 Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2840000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>CESC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2855</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Series D</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Debt</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>BlackSoil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>Peel works</td>\n",
       "      <td>Saas</td>\n",
       "      <td>Debt</td>\n",
       "      <td>408000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2020</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sachin Chhabra</td>\n",
       "      <td>Equanimity Investments</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2310 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Company Name                          Sector Funding Stage  \\\n",
       "0     Thecollegefever  Advertising, marketing & sales          Seed   \n",
       "1     Happy cow dairy          Agriculture & agritech          Seed   \n",
       "2          Myloancare              Financial Services      Series A   \n",
       "3         Payme india              Financial services         Angel   \n",
       "5              Hasura                 Cloud computing          Seed   \n",
       "...               ...                             ...           ...   \n",
       "2853           Werize              Financial services      Series A   \n",
       "2854       Peel works                            Saas      Series D   \n",
       "2855       Peel works                            Saas      Series D   \n",
       "2856       Peel works                            Saas          Debt   \n",
       "2857       Peel works                            Saas          Debt   \n",
       "\n",
       "         Amount Headquarters  \\\n",
       "0     250000.00    Bangalore   \n",
       "1     584000.00       Mumbai   \n",
       "2     949000.00      Gurgaon   \n",
       "3    2000000.00        Noida   \n",
       "5    1600000.00    Bangalore   \n",
       "...         ...          ...   \n",
       "2853 8000000.00    Bangalore   \n",
       "2854 2840000.00       Mumbai   \n",
       "2855 2000000.00       Mumbai   \n",
       "2856 1000000.00       Mumbai   \n",
       "2857  408000.00       Mumbai   \n",
       "\n",
       "                                            Description Funding Year  \\\n",
       "0     TheCollegeFever is a hub for fun, fiesta and f...         2018   \n",
       "1     A startup which aggregates milk from dairy far...         2018   \n",
       "2             Leading Online Loans Marketplace in India         2018   \n",
       "3     PayMe India is an innovative FinTech organizat...         2018   \n",
       "5     Hasura is a platform that allows developers to...         2018   \n",
       "...                                                 ...          ...   \n",
       "2853  India’s first socially distributed full stack ...         2021   \n",
       "2854                                            Unknown         2020   \n",
       "2855                                            Unknown         2020   \n",
       "2856                                            Unknown         2020   \n",
       "2857                                            Unknown         2020   \n",
       "\n",
       "     Year Founded         Founders               Investors  \n",
       "0             nan  Unknown Founder             Undisclosed  \n",
       "1             nan  Unknown Founder             Undisclosed  \n",
       "2             nan  Unknown Founder             Undisclosed  \n",
       "3             nan  Unknown Founder             Undisclosed  \n",
       "5             nan  Unknown Founder             Undisclosed  \n",
       "...           ...              ...                     ...  \n",
       "2853         2019    Vishal Chopra           3one4 Capital  \n",
       "2854         2010   Sachin Chhabra                    CESC  \n",
       "2855         2010   Sachin Chhabra             Undisclosed  \n",
       "2856         2010   Sachin Chhabra               BlackSoil  \n",
       "2857         2010   Sachin Chhabra  Equanimity Investments  \n",
       "\n",
       "[2310 rows x 10 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_set.loc[complete_set[\"Amount\"] > 0.00]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7213c7b",
   "metadata": {},
   "source": [
    "After the final touches to the dataset, we have a total of **2,858 deals** from 2018 to 2021, with 2,310 (80%) deals having disclosed the amounts involved. Since the deals with amounts undisclosed and/or unavailable are about 20% of the complete dataset, it would be prudent to work with the whole dataset as is without exluding the deals without amounts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c027acd7",
   "metadata": {},
   "source": [
    "### 4.1 What We've Learnt So Far"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3a8f4f",
   "metadata": {},
   "source": [
    "1. Bharatpe (10), Byju's (10) and Zomato (7) were the startups involved in most deals over the period. They are headquartered in 3 different locations and operate in 3 different sectors.\n",
    "\n",
    "2. Startups in the education (279), fintech (258), and medicine & healthcare (227) sectors were involved in most transactions over the period, with financial services (166) and e-commerce (158) following in distance. The top 10 out of 595 sectors, by number of deals, make up about 53% of the total deals over the period hence it will be fair to conclude that funding is centralized around a few sectors. That education, fintech and medicine & healthcare are miles ahead of the rest nails it down further.\n",
    "\n",
    "3. For the deals with disclosed rounds/stages, Seed round had the most deals by a mile (924) followed by Series A (310) and Pre-Series A (289), with Series B and Series C following in the distance with 134 and 114 deals respectively.\n",
    "\n",
    "4. Over 16% of the data available are outliers so it would be unwise to remove all of them. At USD 121.27m with them and USD 25.90m without them, the effects of the 2 biggest outliers on the mean is evident. This represents a USD 95.37m difference between the two datasets. In view of these, the median of USD 3m will be used for analyses for the \"average\" startup. i.e. the average funding deal over the period was worth USD 3m.\n",
    "\n",
    "5. Bangalore (916) led the pack (as the city with most startups involved in deals) by almost twice as much as the next location, Mumbai, which had 471 deals. Gurgaon (317), New Delhi (230), Chennai (106), and Pune (104) followed in that order. These made up about 75% of the total transactions over the period, pointing to part acceptance of the null hypothesis which posits that funding to startups is centralized around specific locations and sectors.\n",
    "\n",
    "6. Despite the big drop in 2019, the number of funding deals is increasing per year - from 525 in 2018 to 1,190 in 2021. This implies that chances are fairly high that a new startup would be able to land a funding deal going forward since the number of deals is on an increasing trajectory. \n",
    "\n",
    "7. In line with the increasing number of deals over the period, total amounts invested have been increasing over the period, with investments in 2021 having the highest monetary value. Also, despite 2018 having a higher number of transactions, the average amount per deal (USD 12.7bn) was significantly less than that of 2019 (USD 37.5bn) which had 89 deals.\n",
    "\n",
    "8. Newer startups were involved in more funding deals, with an overall increase from startups founded in 2000. 2021's low value may imply lower chances of landing a funding deal in the same year that a startup was founded. As such, it is fair to say that other sources of finance may be used in the first year of founding before actively seeking a funding deal down the line. This is further highlighted by the observation that startups founded in 2018 raised the most funds over the period, followed by 2006 and then other startups founded after 2009.\n",
    "\n",
    "9. Out of about 1,245 different known investors over the period, Venture Catalysts (52) and Inflection Point Ventures (42) made the most investments, by number of deals. They were followed by Sequoia Capital India (25) and Tiger Global (24).\n",
    "\n",
    "10. Even with the foregoing, Silver Lake's single USD 70bn investment tops the list of investments by value. This is followed in a distance by Salesforce Ventures (2 deals) summing up to USD 3bn, Tiger Global (24 deals) with USD 2.36bn, Facebook (2 deals) with USD 2.31bn, and then General Atlantic (7 deals) with USD 1.65bn.  \n",
    "\n",
    "11. Note 10 above creates the impression that number of deals does not necessarily imply high value of investments since only one of the top four investors involved in deals featured in the top 5 by value of total investments. This is also highlighted by the plot which showed little to no relationship between the two. It is however noteworthy that aside Tiger Global (24), investors with less than 10 transactions seemed to have made more investments by value.\n",
    "\n",
    "12. Using the average value per deal, Salesforce Ventures (USD 1.5bn per deal) make the best investors after Silver Lake (USD 70bn for a single deal). Facebook (USD 1.15bn per deal), Canaan Valley Capital (USD 1bn for a single deal), and Carmignac (USD 800m for a single deal) also follow in that order. These investors who made the biggest investments over the period were involved in less than 5 deals. This implies that whichever startup intends to get funding from any of these investors will have to get their work right. \n",
    "\n",
    "13. Given that the top investors invested in a variety of sectors, it is difficult to conclude that a startup is likely to land a funding deal with these top investors by investing in a particular sector. We must however not rule out the fact that the education sector had the most funding deals over the period and also tops the list (with the tourism & hospitality industry) as the most favoured sector (on average) by the top investors. We can therefore advice that, other things being equal, a startup that intends to get higher than average funding can consider the education sector as its first."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a51840a",
   "metadata": {},
   "source": [
    "## More Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6867df2d",
   "metadata": {},
   "source": [
    "### 4.2 How is funding distributed across deals by location?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3b58d032",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Headquarters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>471</td>\n",
       "      <td>230811847986.00</td>\n",
       "      <td>490046386.38</td>\n",
       "      <td>2000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangalore</th>\n",
       "      <td>916</td>\n",
       "      <td>24170275334.00</td>\n",
       "      <td>26386763.47</td>\n",
       "      <td>2200000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gurgaon</th>\n",
       "      <td>317</td>\n",
       "      <td>6936987680.00</td>\n",
       "      <td>21883241.89</td>\n",
       "      <td>2800000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi</th>\n",
       "      <td>230</td>\n",
       "      <td>3421582173.00</td>\n",
       "      <td>14876444.23</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>6</td>\n",
       "      <td>3078300000.00</td>\n",
       "      <td>513050000.00</td>\n",
       "      <td>1650000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>94</td>\n",
       "      <td>1467760600.00</td>\n",
       "      <td>15614474.47</td>\n",
       "      <td>2500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pune</th>\n",
       "      <td>104</td>\n",
       "      <td>1395938705.00</td>\n",
       "      <td>13422487.55</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>106</td>\n",
       "      <td>1123975826.00</td>\n",
       "      <td>10603545.53</td>\n",
       "      <td>2000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>26</td>\n",
       "      <td>918985000.00</td>\n",
       "      <td>35345576.92</td>\n",
       "      <td>6250000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>88</td>\n",
       "      <td>837104425.00</td>\n",
       "      <td>9512550.28</td>\n",
       "      <td>613650.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count             sum         mean     median\n",
       "Headquarters                                               \n",
       "Mumbai          471 230811847986.00 490046386.38 2000000.00\n",
       "Bangalore       916  24170275334.00  26386763.47 2200000.00\n",
       "Gurgaon         317   6936987680.00  21883241.89 2800000.00\n",
       "New Delhi       230   3421582173.00  14876444.23 1000000.00\n",
       "California        6   3078300000.00 513050000.00 1650000.00\n",
       "Unknown          94   1467760600.00  15614474.47 2500000.00\n",
       "Pune            104   1395938705.00  13422487.55 1000000.00\n",
       "Chennai         106   1123975826.00  10603545.53 2000000.00\n",
       "Haryana          26    918985000.00  35345576.92 6250000.00\n",
       "Delhi            88    837104425.00   9512550.28  613650.00"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_location = complete_set.groupby(by = \"Headquarters\").Amount.agg([\"count\", \"sum\", \"mean\", \"median\"]).sort_values(by = \"sum\", ascending = False)\n",
    "by_location.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d875debf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Investments per City')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = (by_location.index)[:10], x = (by_location[\"sum\"])[:10])\n",
    "plt.xlabel(\"Sum of Amounts Invested\")\n",
    "plt.ylabel(\"Location\")\n",
    "plt.title(\"Distribution of Investments per City\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "74dfbb3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sum total of investments over the period was USD 279777511482.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# What is the sum total of investments over the period?\n",
    "total_investments = complete_set.Amount.sum()\n",
    "print(\"The sum total of investments over the period was USD \" + str(complete_set.Amount.sum()), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "765b9b5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mumbai-based startups took up 82.49835619860022% of total amounts invested over the period. \n",
      " Bangalore-based startups followed with 8.639105840197251% of total amounts invested over the period. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "mumbai_percent = (230811847986.00/total_investments)*100\n",
    "bangalore_percent = (24170275334.00/total_investments)*100\n",
    "\n",
    "print(\"Mumbai-based startups took up \" + str(mumbai_percent) + \"% of total amounts invested over the period.\", \"\\n\", \n",
    "      \"Bangalore-based startups followed with \" + str(bangalore_percent) + \"% of total amounts invested over the period.\", \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2221f3bf",
   "metadata": {},
   "source": [
    "Looking at the total value of deals by location, Mumbai leads with USD 230bn from 471 deals, averaging about USD 490m per deal. Bangalore (916 deals) follows in a distance with USD 24bn at USD 26m per deal. The top 5 is completed with Gurgaon-headquartered  startups (317 deals) receiving USD 6.9bn (USD 21.9m average), New Delhi-headquartered startups (230 deals) receiving USD 3.4bn (USD 14.9m average), and California-based startups (6 deals) receiving USD 3bn in funding at an average of USD 513m.\n",
    "\n",
    "We also note that startups headquartered in Mumbai and Bangalore alone got a cumulative 91% of the total funding over the period. This matches the null hypothesis that funding to startups is centralized around specific locations. *Based on this, the team may be advised to consider Mumbai as the first choice for the headquarters of the potential startup.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2972ab0",
   "metadata": {},
   "source": [
    "### 4.3 Does number of deals translate into funding for the sectors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e9ac99a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education                    278\n",
       "Fintech                      259\n",
       "Medicine & healthcare        227\n",
       "E-commerce                   158\n",
       "Financial services           144\n",
       "Food & Nutrition             137\n",
       "Agriculture & agritech        85\n",
       "Automobiles & automotives     81\n",
       "Logistics & supply chain      70\n",
       "Consumer goods & services     62\n",
       "Name: Sector, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recap of the top 10 sectors by number of deals\n",
    "unique_sectors.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6a5da40a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fintech</th>\n",
       "      <td>259</td>\n",
       "      <td>154792358725.00</td>\n",
       "      <td>597653894.69</td>\n",
       "      <td>5000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>29</td>\n",
       "      <td>70550380000.00</td>\n",
       "      <td>2432771724.14</td>\n",
       "      <td>4000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>278</td>\n",
       "      <td>6380219556.00</td>\n",
       "      <td>22950430.06</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E-commerce</th>\n",
       "      <td>158</td>\n",
       "      <td>4700024800.00</td>\n",
       "      <td>29746992.41</td>\n",
       "      <td>3850000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food &amp; Nutrition</th>\n",
       "      <td>137</td>\n",
       "      <td>4204991000.00</td>\n",
       "      <td>30693364.96</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial services</th>\n",
       "      <td>144</td>\n",
       "      <td>3507928700.00</td>\n",
       "      <td>24360615.97</td>\n",
       "      <td>3350000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech</th>\n",
       "      <td>39</td>\n",
       "      <td>3470983900.00</td>\n",
       "      <td>88999587.18</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automobiles &amp; automotives</th>\n",
       "      <td>81</td>\n",
       "      <td>2962017246.00</td>\n",
       "      <td>36568114.15</td>\n",
       "      <td>3000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medicine &amp; healthcare</th>\n",
       "      <td>227</td>\n",
       "      <td>2802364745.00</td>\n",
       "      <td>12345219.14</td>\n",
       "      <td>1500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multinational conglomerate company</th>\n",
       "      <td>1</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>2200000000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count             sum          mean  \\\n",
       "Sector                                                                    \n",
       "Fintech                               259 154792358725.00  597653894.69   \n",
       "Retail                                 29  70550380000.00 2432771724.14   \n",
       "Education                             278   6380219556.00   22950430.06   \n",
       "E-commerce                            158   4700024800.00   29746992.41   \n",
       "Food & Nutrition                      137   4204991000.00   30693364.96   \n",
       "Financial services                    144   3507928700.00   24360615.97   \n",
       "Tech                                   39   3470983900.00   88999587.18   \n",
       "Automobiles & automotives              81   2962017246.00   36568114.15   \n",
       "Medicine & healthcare                 227   2802364745.00   12345219.14   \n",
       "Multinational conglomerate company      1   2200000000.00 2200000000.00   \n",
       "\n",
       "                                          median  \n",
       "Sector                                            \n",
       "Fintech                               5000000.00  \n",
       "Retail                                4000000.00  \n",
       "Education                             1000000.00  \n",
       "E-commerce                            3850000.00  \n",
       "Food & Nutrition                      1000000.00  \n",
       "Financial services                    3350000.00  \n",
       "Tech                                  1000000.00  \n",
       "Automobiles & automotives             3000000.00  \n",
       "Medicine & healthcare                 1500000.00  \n",
       "Multinational conglomerate company 2200000000.00  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_wise = complete_set.groupby(by = \"Sector\").Amount.agg([\"count\", \"sum\", \"mean\", \"median\"]).sort_values(by = \"sum\", ascending = False)\n",
    "sector_wise.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d5c36784",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Value of Investments by Sector')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the top 10 sectors by total funding received by it's startups\n",
    "sns.barplot(y = sector_wise[:10].index, x = (sector_wise[\"sum\"])[:10])\n",
    "plt.xlabel(\"Total Value of Investment\")\n",
    "plt.ylabel(\"Sector\")\n",
    "plt.title(\"Total Value of Investments by Sector\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4cddceab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.54354959815913"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total percentage of funding to \n",
    "fintech_percent = (154792358725.00/total_investments)*100\n",
    "retail_percent = (70550380000.00/total_investments)*100\n",
    "fintech_percent+retail_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "306a0777",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Median Value of Investments by Sector')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the median amounts invested per sector\n",
    "sns.barplot(y = sector_wise[:9].index, x = (sector_wise[\"median\"])[:9]) # Excluding the \"Multinational conglomerate company\"\n",
    "plt.xlabel(\"Median Value of Investment (in millions)\")\n",
    "plt.ylabel(\"Sector\")\n",
    "plt.title(\"Median Value of Investments by Sector\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da176938",
   "metadata": {},
   "source": [
    "Given that 7 out of the 10 sectors whose startups had the most deals were in the top 10 startups by funding received, we can conclude that number of deals has a positive relationship with the amount of funding. The Fintech sector led this time, receiving a total of USD 154bn with USD 5m as the median amount. Retail (USD 70bn total) came second with a USD 4m median, followed in order by the education (USD 6bn total) with a USD 1m median, e-commerce (USD 4.7bn total) with a USD 3.85m, and food & nutirition (USD 5bn total)\n",
    "\n",
    "From the second graph, we note that by total amounts invested, the Fintech sector (55%) and the Retail sector (25%) make up 80% of total funding received. By median value of investments, Fintech still leads the race, followed by Retail, E-commerce, Financial Services, and Automobiles and Automotives, in that order.\n",
    "\n",
    "These add to the assertion that funding is centralized around specific sectors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23d86ac",
   "metadata": {},
   "source": [
    "### 4.4 What is the average amount of funding for start-ups in: the sector with most funding, and the location with most funding?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb1a93f",
   "metadata": {},
   "source": [
    "From earlier analyses, we know that the sector with the most funding is the Fintech sector with a total of USD 154bn, an average of USD 597m per deal, and USD 5m per deal median. For the location with most funding, we learnt that it was Mumbai with a total of USD 230bn, an average of USD 490m per deal and a USD 2m median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c1c516eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           2858.00\n",
       "mean        97892761.19\n",
       "std       3096895060.38\n",
       "min                0.00\n",
       "25%           204750.00\n",
       "50%          1600000.00\n",
       "75%          8000000.00\n",
       "max     150000000000.00\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reminding ourselves of the summary stats of the amounts involved in deals over the period.\n",
    "complete_set[\"Amount\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b30614",
   "metadata": {},
   "source": [
    "From the description of the Amount column, we learn that the average deal over the period was worth USD 97m on average, and USD 1.6m median. \n",
    "\n",
    "Great. So how much did startups at the intersection of Mumbai and the Fintech sector receive per deal, and how does it compare to the sector, location, and general averages?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fccfdf36",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Funding Stage</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Description</th>\n",
       "      <th>Funding Year</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Livquik</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>2700000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>LivQuik was incorporated with the vision to si...</td>\n",
       "      <td>2018</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Taxbuddy.com</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>TaxBuddy for any tax related issues.</td>\n",
       "      <td>2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>Sujit Bangar</td>\n",
       "      <td>Zenith Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Coindcx</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series B</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>CoinDCX is a company that specializes in crypt...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2017</td>\n",
       "      <td>Neeraj Khandelwal</td>\n",
       "      <td>Coinbase Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>Blacksoil</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Blacksoil Advisory is an independent boutique ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2016</td>\n",
       "      <td>Ankur Bansal</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>Flexiloans</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>an online lending platform started with an end...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2016</td>\n",
       "      <td>Abhishek Kothari</td>\n",
       "      <td>&gt;Vikram Sud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>Credable</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>4500000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>CredAble provides working capital &amp; supply cha...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2017</td>\n",
       "      <td>Nirav Choksi</td>\n",
       "      <td>Oaks Asset Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>Getvantage</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>GetVantage is India and Southeast Asia's first...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>Amit Srivastava</td>\n",
       "      <td>Samyakth Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Uni</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>18500000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>The team aims to bridge the gap for credit car...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Nitin Gupta</td>\n",
       "      <td>Lightspeed India Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Credit wise capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>CreditWise Capital is a consumer finance company.</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>Hirak Joshi</td>\n",
       "      <td>Venture Catalysts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>Avanse financial services</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>15000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Avanse, regulated by the Reserve Bank of India...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2012</td>\n",
       "      <td>Amit Gainda</td>\n",
       "      <td>International Finance Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>Inflexor ventures</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Experienced Venture Capital-General Partner fo...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>Parampara Capital</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>Pulse</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>750000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Helps people navigate through and profit off f...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2015</td>\n",
       "      <td>Amit Dhakad</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>Citycash</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Focusses on creating a transit led micro-payme...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2017</td>\n",
       "      <td>Vineet Toshniwala</td>\n",
       "      <td>Orios Venture Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>Eduvanz</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series A</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Digital finance NBFC that provides affordable ...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2016</td>\n",
       "      <td>Varun Chopra</td>\n",
       "      <td>Sequoia India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>Godutch</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>1700000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Group Payments platform</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Aniruddh Singh</td>\n",
       "      <td>Matrix India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>Jai kisan</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>3937000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A platform that caters the need of customers i...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2017</td>\n",
       "      <td>Arjun Ahluwalia</td>\n",
       "      <td>Arkam Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Homecapital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Home Down Payment Assistance Program</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lalit Menghani</td>\n",
       "      <td>Varanium NexGen Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>Mintoak</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Merchant payment solutions startup</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Raman Khanduja</td>\n",
       "      <td>Pravega Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>Fingpay</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series A</td>\n",
       "      <td>3500000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Provides payment solutions</td>\n",
       "      <td>2020</td>\n",
       "      <td>2016</td>\n",
       "      <td>Pratyush Halen</td>\n",
       "      <td>IvyCap Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>Smecorner</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series B</td>\n",
       "      <td>30000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Financial lending firm to SMEs</td>\n",
       "      <td>2020</td>\n",
       "      <td>2014</td>\n",
       "      <td>Bhatia</td>\n",
       "      <td>Paragon Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>Supermoney</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Provides financial tools for effective busines...</td>\n",
       "      <td>2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>Nikhil Banerjee</td>\n",
       "      <td>Unitus Ventures.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>Getvantage</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Revenue based finance provider</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Bhavik Vasa</td>\n",
       "      <td>Samyakth Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>Monech</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Automated platform which will help users to ta...</td>\n",
       "      <td>2020</td>\n",
       "      <td>nan</td>\n",
       "      <td>Unknown Founder</td>\n",
       "      <td>Angel investors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>Shopse</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>5500000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Make purchases online or at neighborhood store...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Pallav Jain</td>\n",
       "      <td>Chiratae Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>Alteria capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>150000000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Alteria Capital is a Venture debt firm .</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Vinod Murali</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>Oto capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>The Future Of Vehicle Ownership!</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018</td>\n",
       "      <td>Harsh Saruparia</td>\n",
       "      <td>Matrix Partners India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>Marquee equity</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>3800000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>The World's #1 Fundraising Service - Investor ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2016</td>\n",
       "      <td>Ash Narain</td>\n",
       "      <td>Capital One</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Sarvagram</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series B</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>SarvaGram provides livelihood upgradation for ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019</td>\n",
       "      <td>Utpal Isser</td>\n",
       "      <td>Elevation Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>Eduvanz</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Eduvanz is new, innovative finance company, wh...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2016</td>\n",
       "      <td>Atul Sashittal</td>\n",
       "      <td>InCred Financial Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>Ssa finserv</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Small businesses-focused non-banking lender</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019</td>\n",
       "      <td>Vikas Agarwal</td>\n",
       "      <td>Blue Ashva Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2098</th>\n",
       "      <td>Blacksoil</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>4000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Blacksoil Advisory is an independent boutique ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2016</td>\n",
       "      <td>Ankur Bansal</td>\n",
       "      <td>Awign Enterprises</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>Fable fintech</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series A</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Fable Fintech is being seed-funded by the CCAv...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015</td>\n",
       "      <td>Naushad Contractor</td>\n",
       "      <td>Paytm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>Flexmoney</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series A</td>\n",
       "      <td>4800000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Flexmoney’s mission is to address the enormous...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2015</td>\n",
       "      <td>Yezdi Lashkari</td>\n",
       "      <td>Pravega Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>Upside ai</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Upside AI on the thesis that technology will m...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2017</td>\n",
       "      <td>Kanika Agarrwal</td>\n",
       "      <td>Endiya Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>Digisparsh</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>DigiSparsh is India's first and only healthcar...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Saurabh Soni</td>\n",
       "      <td>GoAhead Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2320</th>\n",
       "      <td>Bimape</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>BimaPe demystifies Insurance and its Managemen...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Rahul Mathur</td>\n",
       "      <td>iSeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Moneyboxx</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Debt</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Moneyboxx Finance provides easy access to fina...</td>\n",
       "      <td>2021</td>\n",
       "      <td>1994</td>\n",
       "      <td>Deepak Aggarwal</td>\n",
       "      <td>Ashv Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>Svasti microfinance</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>4000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Svasti works with urban slum dwellers women mi...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2008</td>\n",
       "      <td>Arunkumar Padmanabhan</td>\n",
       "      <td>Kayenne Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>Ankur capital</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Early stage venture capital fund focused on op...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Ritu Verma</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>Firmway</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Firmway is a platform that digitizes balance c...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2016</td>\n",
       "      <td>Prashant Gupta</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>Bimaplan</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Affordable insurance for the next billion Indians</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Vikul Goyal</td>\n",
       "      <td>Titan Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>Blacksoil nbfc</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>BlackSoil Capital has transformed into an alte...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2010</td>\n",
       "      <td>Mohinder Pal Bansal</td>\n",
       "      <td>Undisclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2443</th>\n",
       "      <td>Blume ventures</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Blume Ventures provides pre-series A and early...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2011</td>\n",
       "      <td>Karthik Reddy</td>\n",
       "      <td>Avendus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477</th>\n",
       "      <td>Kodo card</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Seed</td>\n",
       "      <td>8000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Kodo card is a corporate card for new economy ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2019</td>\n",
       "      <td>Deepti Sanghi</td>\n",
       "      <td>Brex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2482</th>\n",
       "      <td>Jai kisan</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Series A</td>\n",
       "      <td>30000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Jai Kisan is a fintech platform that provides ...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2017</td>\n",
       "      <td>Adriel Maniego</td>\n",
       "      <td>The Chatterjee Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>We founder circle</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-seed</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A global platform of founders for founders off...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Neeraj Tyagi</td>\n",
       "      <td>Geekster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>Arthya wealth and investments</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Pre-series A</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A comprehensive, high quality, open-architectu...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Gaurav Arora</td>\n",
       "      <td>Rishi Kumar Bagla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2787</th>\n",
       "      <td>Easiloan</td>\n",
       "      <td>Fintech</td>\n",
       "      <td>Undisclosed</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>A Fintech startup, offering fast &amp; easy digita...</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>Pramod Kathuria</td>\n",
       "      <td>Tomorrow Capital</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Company Name   Sector Funding Stage          Amount  \\\n",
       "453                         Livquik  Fintech     Corporate      2700000.00   \n",
       "636                    Taxbuddy.com  Fintech   Undisclosed      1000000.00   \n",
       "639                         Coindcx  Fintech      Series B     10000000.00   \n",
       "764                       Blacksoil  Fintech   Undisclosed     10000000.00   \n",
       "805                      Flexiloans  Fintech          Debt      6000000.00   \n",
       "809                        Credable  Fintech   Undisclosed      4500000.00   \n",
       "830                      Getvantage  Fintech          Seed      5000000.00   \n",
       "889                             Uni  Fintech          Seed     18500000.00   \n",
       "890             Credit wise capital  Fintech          Seed      6000000.00   \n",
       "919       Avanse financial services  Fintech   Undisclosed     15000000.00   \n",
       "941               Inflexor ventures  Fintech   Undisclosed     10000000.00   \n",
       "1062                          Pulse  Fintech  Pre-series A       750000.00   \n",
       "1132                       Citycash  Fintech          Seed      1000000.00   \n",
       "1136                        Eduvanz  Fintech      Series A      5000000.00   \n",
       "1223                        Godutch  Fintech          Seed      1700000.00   \n",
       "1247                      Jai kisan  Fintech  Pre-series A      3937000.00   \n",
       "1292                    Homecapital  Fintech   Undisclosed            0.00   \n",
       "1327                        Mintoak  Fintech  Pre-series A      2000000.00   \n",
       "1415                        Fingpay  Fintech      Series A      3500000.00   \n",
       "1481                      Smecorner  Fintech      Series B     30000000.00   \n",
       "1554                     Supermoney  Fintech  Pre-series A      1000000.00   \n",
       "1586                     Getvantage  Fintech          Seed            0.00   \n",
       "1610                         Monech  Fintech          Seed        12700.00   \n",
       "1726                         Shopse  Fintech   Undisclosed      5500000.00   \n",
       "1737                Alteria capital  Fintech          Debt 150000000000.00   \n",
       "1755                    Oto capital  Fintech   Undisclosed      6000000.00   \n",
       "1971                 Marquee equity  Fintech   Undisclosed      3800000.00   \n",
       "1997                      Sarvagram  Fintech      Series B     10000000.00   \n",
       "2000                        Eduvanz  Fintech          Debt     10000000.00   \n",
       "2065                    Ssa finserv  Fintech   Undisclosed      3000000.00   \n",
       "2098                      Blacksoil  Fintech   Undisclosed      4000000.00   \n",
       "2254                  Fable fintech  Fintech      Series A            0.00   \n",
       "2288                      Flexmoney  Fintech      Series A      4800000.00   \n",
       "2303                      Upside ai  Fintech          Seed      1200000.00   \n",
       "2314                     Digisparsh  Fintech          Seed            0.00   \n",
       "2320                         Bimape  Fintech      Pre-seed       500000.00   \n",
       "2371                      Moneyboxx  Fintech          Debt      3000000.00   \n",
       "2409            Svasti microfinance  Fintech   Undisclosed      4000000.00   \n",
       "2412                  Ankur capital  Fintech   Undisclosed     40000000.00   \n",
       "2414                        Firmway  Fintech      Pre-seed            0.00   \n",
       "2417                       Bimaplan  Fintech          Seed       500000.00   \n",
       "2420                 Blacksoil nbfc  Fintech   Undisclosed      3000000.00   \n",
       "2443                 Blume ventures  Fintech   Undisclosed     40000000.00   \n",
       "2477                      Kodo card  Fintech          Seed      8000000.00   \n",
       "2482                      Jai kisan  Fintech      Series A     30000000.00   \n",
       "2496              We founder circle  Fintech      Pre-seed      2000000.00   \n",
       "2512  Arthya wealth and investments  Fintech  Pre-series A      1000000.00   \n",
       "2787                       Easiloan  Fintech   Undisclosed      1000000.00   \n",
       "\n",
       "     Headquarters                                        Description  \\\n",
       "453        Mumbai  LivQuik was incorporated with the vision to si...   \n",
       "636        Mumbai               TaxBuddy for any tax related issues.   \n",
       "639        Mumbai  CoinDCX is a company that specializes in crypt...   \n",
       "764        Mumbai  Blacksoil Advisory is an independent boutique ...   \n",
       "805        Mumbai  an online lending platform started with an end...   \n",
       "809        Mumbai  CredAble provides working capital & supply cha...   \n",
       "830        Mumbai  GetVantage is India and Southeast Asia's first...   \n",
       "889        Mumbai  The team aims to bridge the gap for credit car...   \n",
       "890        Mumbai  CreditWise Capital is a consumer finance company.   \n",
       "919        Mumbai  Avanse, regulated by the Reserve Bank of India...   \n",
       "941        Mumbai  Experienced Venture Capital-General Partner fo...   \n",
       "1062       Mumbai  Helps people navigate through and profit off f...   \n",
       "1132       Mumbai  Focusses on creating a transit led micro-payme...   \n",
       "1136       Mumbai  Digital finance NBFC that provides affordable ...   \n",
       "1223       Mumbai                            Group Payments platform   \n",
       "1247       Mumbai  A platform that caters the need of customers i...   \n",
       "1292       Mumbai               Home Down Payment Assistance Program   \n",
       "1327       Mumbai                 Merchant payment solutions startup   \n",
       "1415       Mumbai                         Provides payment solutions   \n",
       "1481       Mumbai                     Financial lending firm to SMEs   \n",
       "1554       Mumbai  Provides financial tools for effective busines...   \n",
       "1586       Mumbai                     Revenue based finance provider   \n",
       "1610       Mumbai  Automated platform which will help users to ta...   \n",
       "1726       Mumbai  Make purchases online or at neighborhood store...   \n",
       "1737       Mumbai           Alteria Capital is a Venture debt firm .   \n",
       "1755       Mumbai                   The Future Of Vehicle Ownership!   \n",
       "1971       Mumbai  The World's #1 Fundraising Service - Investor ...   \n",
       "1997       Mumbai  SarvaGram provides livelihood upgradation for ...   \n",
       "2000       Mumbai  Eduvanz is new, innovative finance company, wh...   \n",
       "2065       Mumbai        Small businesses-focused non-banking lender   \n",
       "2098       Mumbai  Blacksoil Advisory is an independent boutique ...   \n",
       "2254       Mumbai  Fable Fintech is being seed-funded by the CCAv...   \n",
       "2288       Mumbai  Flexmoney’s mission is to address the enormous...   \n",
       "2303       Mumbai  Upside AI on the thesis that technology will m...   \n",
       "2314       Mumbai  DigiSparsh is India's first and only healthcar...   \n",
       "2320       Mumbai  BimaPe demystifies Insurance and its Managemen...   \n",
       "2371       Mumbai  Moneyboxx Finance provides easy access to fina...   \n",
       "2409       Mumbai  Svasti works with urban slum dwellers women mi...   \n",
       "2412       Mumbai  Early stage venture capital fund focused on op...   \n",
       "2414       Mumbai  Firmway is a platform that digitizes balance c...   \n",
       "2417       Mumbai  Affordable insurance for the next billion Indians   \n",
       "2420       Mumbai  BlackSoil Capital has transformed into an alte...   \n",
       "2443       Mumbai  Blume Ventures provides pre-series A and early...   \n",
       "2477       Mumbai  Kodo card is a corporate card for new economy ...   \n",
       "2482       Mumbai  Jai Kisan is a fintech platform that provides ...   \n",
       "2496       Mumbai  A global platform of founders for founders off...   \n",
       "2512       Mumbai  A comprehensive, high quality, open-architectu...   \n",
       "2787       Mumbai  A Fintech startup, offering fast & easy digita...   \n",
       "\n",
       "     Funding Year Year Founded               Founders  \\\n",
       "453          2018          nan        Unknown Founder   \n",
       "636          2020         2019           Sujit Bangar   \n",
       "639          2020         2017      Neeraj Khandelwal   \n",
       "764          2020         2016           Ankur Bansal   \n",
       "805          2020         2016       Abhishek Kothari   \n",
       "809          2020         2017           Nirav Choksi   \n",
       "830          2020         2019        Amit Srivastava   \n",
       "889          2020          nan            Nitin Gupta   \n",
       "890          2020         2018            Hirak Joshi   \n",
       "919          2020         2012            Amit Gainda   \n",
       "941          2020         2020      Parampara Capital   \n",
       "1062         2020         2015            Amit Dhakad   \n",
       "1132         2020         2017      Vineet Toshniwala   \n",
       "1136         2020         2016           Varun Chopra   \n",
       "1223         2020          nan         Aniruddh Singh   \n",
       "1247         2020         2017        Arjun Ahluwalia   \n",
       "1292         2020          nan         Lalit Menghani   \n",
       "1327         2020          nan         Raman Khanduja   \n",
       "1415         2020         2016         Pratyush Halen   \n",
       "1481         2020         2014                 Bhatia   \n",
       "1554         2020         2018        Nikhil Banerjee   \n",
       "1586         2020          nan            Bhavik Vasa   \n",
       "1610         2020          nan        Unknown Founder   \n",
       "1726         2021         2020            Pallav Jain   \n",
       "1737         2021         2018           Vinod Murali   \n",
       "1755         2021         2018        Harsh Saruparia   \n",
       "1971         2021         2016             Ash Narain   \n",
       "1997         2021         2019            Utpal Isser   \n",
       "2000         2021         2016         Atul Sashittal   \n",
       "2065         2021         2019          Vikas Agarwal   \n",
       "2098         2021         2016           Ankur Bansal   \n",
       "2254         2021         2015     Naushad Contractor   \n",
       "2288         2021         2015         Yezdi Lashkari   \n",
       "2303         2021         2017        Kanika Agarrwal   \n",
       "2314         2021         2020           Saurabh Soni   \n",
       "2320         2021         2020           Rahul Mathur   \n",
       "2371         2021         1994        Deepak Aggarwal   \n",
       "2409         2021         2008  Arunkumar Padmanabhan   \n",
       "2412         2021         2011             Ritu Verma   \n",
       "2414         2021         2016         Prashant Gupta   \n",
       "2417         2021         2020            Vikul Goyal   \n",
       "2420         2021         2010    Mohinder Pal Bansal   \n",
       "2443         2021         2011          Karthik Reddy   \n",
       "2477         2021         2019          Deepti Sanghi   \n",
       "2482         2021         2017         Adriel Maniego   \n",
       "2496         2021         2020           Neeraj Tyagi   \n",
       "2512         2021         2020           Gaurav Arora   \n",
       "2787         2021         2020        Pramod Kathuria   \n",
       "\n",
       "                              Investors  \n",
       "453                         Undisclosed  \n",
       "636                       Zenith Global  \n",
       "639                   Coinbase Ventures  \n",
       "764                         Undisclosed  \n",
       "805                         >Vikram Sud  \n",
       "809               Oaks Asset Management  \n",
       "830                    Samyakth Capital  \n",
       "889           Lightspeed India Partners  \n",
       "890                   Venture Catalysts  \n",
       "919   International Finance Corporation  \n",
       "941                         Undisclosed  \n",
       "1062                        Undisclosed  \n",
       "1132             Orios Venture Partners  \n",
       "1136                      Sequoia India  \n",
       "1223                       Matrix India  \n",
       "1247                     Arkam Ventures  \n",
       "1292               Varanium NexGen Fund  \n",
       "1327                   Pravega Ventures  \n",
       "1415                    IvyCap Ventures  \n",
       "1481                   Paragon Partners  \n",
       "1554                   Unitus Ventures.  \n",
       "1586                   Samyakth Capital  \n",
       "1610                    Angel investors  \n",
       "1726                  Chiratae Ventures  \n",
       "1737                        Undisclosed  \n",
       "1755              Matrix Partners India  \n",
       "1971                        Capital One  \n",
       "1997                  Elevation Capital  \n",
       "2000          InCred Financial Services  \n",
       "2065                 Blue Ashva Capital  \n",
       "2098                  Awign Enterprises  \n",
       "2254                              Paytm  \n",
       "2288                   Pravega Ventures  \n",
       "2303                    Endiya Partners  \n",
       "2314                   GoAhead Ventures  \n",
       "2320                              iSeed  \n",
       "2371                       Ashv Finance  \n",
       "2409                   Kayenne Ventures  \n",
       "2412                        Undisclosed  \n",
       "2414                        Undisclosed  \n",
       "2417                      Titan Capital  \n",
       "2420                        Undisclosed  \n",
       "2443                            Avendus  \n",
       "2477                               Brex  \n",
       "2482               The Chatterjee Group  \n",
       "2496                           Geekster  \n",
       "2512                  Rishi Kumar Bagla  \n",
       "2787                   Tomorrow Capital  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data for deals involving startups in the Fintech sector and headquartered in Mumbai\n",
    "max_intersection = complete_set.loc[(complete_set[\"Headquarters\"] == \"Mumbai\") & \n",
    "                (complete_set[\"Sector\"] == \"Fintech\")]\n",
    "max_intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a68fe689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150318899700.0 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3131643743.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21649657875.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3868500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150000000000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Amount\n",
       "count           48.00\n",
       "mean    3131643743.75\n",
       "std    21649657875.46\n",
       "min              0.00\n",
       "25%        1000000.00\n",
       "50%        3868500.00\n",
       "75%        8500000.00\n",
       "max   150000000000.00"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(max_intersection[\"Amount\"].sum(), \"\\n\")\n",
    "max_intersection.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa42a35",
   "metadata": {},
   "source": [
    "First we note that startups that fall within the intersection of Mumbai and Fintech were involved in a total of 48 deals worth a cumulative USD 150bn over the period.\n",
    "\n",
    "Second, we note that each deal had an average worth of about USD 3bn, and a median of USD 3.8m. This implies that startups in the Fintech sector and headquartered in Mumbai receive more funding per deal on average. The median per deal is second to the median funding per deal of the Fintech sector. Based on these, it is safe to say that the intersection of Fintech and Mumbai is a sweet spot anyone who intends to venture into the Indian startup space may want to explore.\n",
    "\n",
    "The above data is summarized in the below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6a93da50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grouping</th>\n",
       "      <th>Mean Funding</th>\n",
       "      <th>Median Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>General</td>\n",
       "      <td>97,892,761.19</td>\n",
       "      <td>1,600,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fintech sector</td>\n",
       "      <td>597,653,894.69</td>\n",
       "      <td>5,000,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mumbai-based</td>\n",
       "      <td>490,046,386.38</td>\n",
       "      <td>2,000,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fintech &amp; Mumbai</td>\n",
       "      <td>3,131,643,743.75</td>\n",
       "      <td>3,868,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Grouping      Mean Funding Median Funding\n",
       "0           General     97,892,761.19   1,600,000.00\n",
       "1    Fintech sector    597,653,894.69   5,000,000.00\n",
       "2      Mumbai-based    490,046,386.38   2,000,000.00\n",
       "3  Fintech & Mumbai  3,131,643,743.75   3,868,500.00"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"Grouping\":[\"General\",\"Fintech sector\",\"Mumbai-based\",\"Fintech & Mumbai\"],\n",
    "       \"Mean Funding\":[\"97,892,761.19\", \"597,653,894.69\", \"490,046,386.38\", \"3,131,643,743.75\"],\n",
    "       \"Median Funding\":[\"1,600,000.00\", \"5,000,000.00\", \"2,000,000.00\", \"3,868,500.00\"]}\n",
    "data_summarized = pd.DataFrame(data)\n",
    "data_summarized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0987f1e3",
   "metadata": {},
   "source": [
    "### 4.5 How does the breakdown by stages of funding look?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2dadfe21",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Funding Stage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Debt</th>\n",
       "      <td>61</td>\n",
       "      <td>150735971025.00</td>\n",
       "      <td>2471081492.21</td>\n",
       "      <td>5000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undisclosed</th>\n",
       "      <td>924</td>\n",
       "      <td>99637358054.00</td>\n",
       "      <td>107832638.59</td>\n",
       "      <td>1500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series C</th>\n",
       "      <td>114</td>\n",
       "      <td>5394693700.00</td>\n",
       "      <td>47321874.56</td>\n",
       "      <td>30000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series B</th>\n",
       "      <td>134</td>\n",
       "      <td>3755631600.00</td>\n",
       "      <td>28027101.49</td>\n",
       "      <td>16000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series D</th>\n",
       "      <td>53</td>\n",
       "      <td>3645842160.00</td>\n",
       "      <td>68789474.72</td>\n",
       "      <td>53000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series E</th>\n",
       "      <td>29</td>\n",
       "      <td>3430977000.00</td>\n",
       "      <td>118309551.72</td>\n",
       "      <td>96000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series A</th>\n",
       "      <td>310</td>\n",
       "      <td>2949612920.00</td>\n",
       "      <td>9514880.39</td>\n",
       "      <td>5000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series H</th>\n",
       "      <td>4</td>\n",
       "      <td>2312500000.00</td>\n",
       "      <td>578125000.00</td>\n",
       "      <td>635000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private Equity</th>\n",
       "      <td>11</td>\n",
       "      <td>2154000000.00</td>\n",
       "      <td>195818181.82</td>\n",
       "      <td>41900000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series F</th>\n",
       "      <td>11</td>\n",
       "      <td>1703200000.00</td>\n",
       "      <td>154836363.64</td>\n",
       "      <td>55000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seed</th>\n",
       "      <td>693</td>\n",
       "      <td>1046707774.00</td>\n",
       "      <td>1510400.83</td>\n",
       "      <td>500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series G</th>\n",
       "      <td>3</td>\n",
       "      <td>593000000.00</td>\n",
       "      <td>197666666.67</td>\n",
       "      <td>266000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-series A</th>\n",
       "      <td>289</td>\n",
       "      <td>578711533.00</td>\n",
       "      <td>2002462.05</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post-IPO Equity</th>\n",
       "      <td>3</td>\n",
       "      <td>430163200.00</td>\n",
       "      <td>143387733.33</td>\n",
       "      <td>185000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venture</th>\n",
       "      <td>38</td>\n",
       "      <td>348024036.00</td>\n",
       "      <td>9158527.26</td>\n",
       "      <td>3383000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corporate</th>\n",
       "      <td>8</td>\n",
       "      <td>282796000.00</td>\n",
       "      <td>35349500.00</td>\n",
       "      <td>2000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Secondary Market</th>\n",
       "      <td>3</td>\n",
       "      <td>226498000.00</td>\n",
       "      <td>75499333.33</td>\n",
       "      <td>16498000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-series B</th>\n",
       "      <td>18</td>\n",
       "      <td>111382000.00</td>\n",
       "      <td>6187888.89</td>\n",
       "      <td>4500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post-IPO Debt</th>\n",
       "      <td>2</td>\n",
       "      <td>59200000.00</td>\n",
       "      <td>29600000.00</td>\n",
       "      <td>29600000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Funding</th>\n",
       "      <td>1</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>50000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series B+</th>\n",
       "      <td>1</td>\n",
       "      <td>49400000.00</td>\n",
       "      <td>49400000.00</td>\n",
       "      <td>49400000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series D1</th>\n",
       "      <td>2</td>\n",
       "      <td>46967000.00</td>\n",
       "      <td>23483500.00</td>\n",
       "      <td>23483500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-series C</th>\n",
       "      <td>3</td>\n",
       "      <td>46743000.00</td>\n",
       "      <td>15581000.00</td>\n",
       "      <td>4000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-seed</th>\n",
       "      <td>73</td>\n",
       "      <td>30555000.00</td>\n",
       "      <td>418561.64</td>\n",
       "      <td>200000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-series</th>\n",
       "      <td>3</td>\n",
       "      <td>29000000.00</td>\n",
       "      <td>9666666.67</td>\n",
       "      <td>8000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series E2</th>\n",
       "      <td>1</td>\n",
       "      <td>26500000.00</td>\n",
       "      <td>26500000.00</td>\n",
       "      <td>26500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series B2</th>\n",
       "      <td>2</td>\n",
       "      <td>22000000.00</td>\n",
       "      <td>11000000.00</td>\n",
       "      <td>11000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series B3</th>\n",
       "      <td>1</td>\n",
       "      <td>14300000.00</td>\n",
       "      <td>14300000.00</td>\n",
       "      <td>14300000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angel</th>\n",
       "      <td>42</td>\n",
       "      <td>13673460.00</td>\n",
       "      <td>325558.57</td>\n",
       "      <td>35220.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bridge</th>\n",
       "      <td>9</td>\n",
       "      <td>12500000.00</td>\n",
       "      <td>1388888.89</td>\n",
       "      <td>500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post series A</th>\n",
       "      <td>1</td>\n",
       "      <td>11000000.00</td>\n",
       "      <td>11000000.00</td>\n",
       "      <td>11000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series A+</th>\n",
       "      <td>1</td>\n",
       "      <td>8500000.00</td>\n",
       "      <td>8500000.00</td>\n",
       "      <td>8500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series F1</th>\n",
       "      <td>1</td>\n",
       "      <td>7000000.00</td>\n",
       "      <td>7000000.00</td>\n",
       "      <td>7000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edge</th>\n",
       "      <td>1</td>\n",
       "      <td>5040000.00</td>\n",
       "      <td>5040000.00</td>\n",
       "      <td>5040000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series F2</th>\n",
       "      <td>1</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>5000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grant</th>\n",
       "      <td>4</td>\n",
       "      <td>1524020.00</td>\n",
       "      <td>381005.00</td>\n",
       "      <td>13760.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series A-1</th>\n",
       "      <td>1</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid series</th>\n",
       "      <td>1</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-equity Assistance</th>\n",
       "      <td>1</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>40000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count             sum          mean       median\n",
       "Funding Stage                                                          \n",
       "Debt                      61 150735971025.00 2471081492.21   5000000.00\n",
       "Undisclosed              924  99637358054.00  107832638.59   1500000.00\n",
       "Series C                 114   5394693700.00   47321874.56  30000000.00\n",
       "Series B                 134   3755631600.00   28027101.49  16000000.00\n",
       "Series D                  53   3645842160.00   68789474.72  53000000.00\n",
       "Series E                  29   3430977000.00  118309551.72  96000000.00\n",
       "Series A                 310   2949612920.00    9514880.39   5000000.00\n",
       "Series H                   4   2312500000.00  578125000.00 635000000.00\n",
       "Private Equity            11   2154000000.00  195818181.82  41900000.00\n",
       "Series F                  11   1703200000.00  154836363.64  55000000.00\n",
       "Seed                     693   1046707774.00    1510400.83    500000.00\n",
       "Series G                   3    593000000.00  197666666.67 266000000.00\n",
       "Pre-series A             289    578711533.00    2002462.05   1000000.00\n",
       "Post-IPO Equity            3    430163200.00  143387733.33 185000000.00\n",
       "Venture                   38    348024036.00    9158527.26   3383000.00\n",
       "Corporate                  8    282796000.00   35349500.00   2000000.00\n",
       "Secondary Market           3    226498000.00   75499333.33  16498000.00\n",
       "Pre-series B              18    111382000.00    6187888.89   4500000.00\n",
       "Post-IPO Debt              2     59200000.00   29600000.00  29600000.00\n",
       "Funding                    1     50000000.00   50000000.00  50000000.00\n",
       "Series B+                  1     49400000.00   49400000.00  49400000.00\n",
       "Series D1                  2     46967000.00   23483500.00  23483500.00\n",
       "Pre-series C               3     46743000.00   15581000.00   4000000.00\n",
       "Pre-seed                  73     30555000.00     418561.64    200000.00\n",
       "Pre-series                 3     29000000.00    9666666.67   8000000.00\n",
       "Series E2                  1     26500000.00   26500000.00  26500000.00\n",
       "Series B2                  2     22000000.00   11000000.00  11000000.00\n",
       "Series B3                  1     14300000.00   14300000.00  14300000.00\n",
       "Angel                     42     13673460.00     325558.57     35220.00\n",
       "Bridge                     9     12500000.00    1388888.89    500000.00\n",
       "Post series A              1     11000000.00   11000000.00  11000000.00\n",
       "Series A+                  1      8500000.00    8500000.00   8500000.00\n",
       "Series F1                  1      7000000.00    7000000.00   7000000.00\n",
       "Edge                       1      5040000.00    5040000.00   5040000.00\n",
       "Series F2                  1      5000000.00    5000000.00   5000000.00\n",
       "Grant                      4      1524020.00     381005.00     13760.00\n",
       "Series A-1                 1      1000000.00    1000000.00   1000000.00\n",
       "Mid series                 1       500000.00     500000.00    500000.00\n",
       "Non-equity Assistance      1        40000.00      40000.00     40000.00"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage_wise = complete_set.groupby(by = \"Funding Stage\").Amount.agg([\"count\", \"sum\", \"mean\", \"median\"]).sort_values(\n",
    "    by = \"sum\", ascending = False)\n",
    "stage_wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0b128275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Amount Invested per Funding Stage/Round')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the top 10 stages/rounds of funding in terms of amounts invested\n",
    "sns.barplot(y = stage_wise[:10].index, x = (stage_wise[\"sum\"])[:10])\n",
    "plt.xlabel(\"Funding Stage\")\n",
    "plt.ylabel(\"Total Amount Invested\")\n",
    "plt.title(\"Total Amount Invested per Funding Stage/Round\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28c598c",
   "metadata": {},
   "source": [
    "As noted earlier, a large portion of the funding stages was undisclosed. For those that were disclosed, a debt (61 deals) was the most common type of funding with a total investment of USD 150bn (USD 2bn average). This was followed in the distance by Series C (USD 5bn), Series B (USD 3.8bn), and Series D (USD 3.6), with each averaging USD 47m, USD 28m, and USD 69m respectively.\n",
    "\n",
    "The Seed round, which had the highest number of deals*, had about USD 1.5m per deal, which should be good enough to hold a startup down for sometime as it focuses on survival and other funding sources."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eee1d7f",
   "metadata": {},
   "source": [
    "### 4.6 Which startups were most favoured by investors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "9ec4f995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alteria capital</th>\n",
       "      <td>1</td>\n",
       "      <td>150000000000.00</td>\n",
       "      <td>150000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reliance retail ventures ltd</th>\n",
       "      <td>1</td>\n",
       "      <td>70000000000.00</td>\n",
       "      <td>70000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowflake</th>\n",
       "      <td>1</td>\n",
       "      <td>3000000000.00</td>\n",
       "      <td>3000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Byju's</th>\n",
       "      <td>10</td>\n",
       "      <td>2522000000.00</td>\n",
       "      <td>252200000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reliance</th>\n",
       "      <td>1</td>\n",
       "      <td>2200000000.00</td>\n",
       "      <td>2200000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swiggy</th>\n",
       "      <td>4</td>\n",
       "      <td>1956000000.00</td>\n",
       "      <td>489000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oyo</th>\n",
       "      <td>6</td>\n",
       "      <td>1665000000.00</td>\n",
       "      <td>277500000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Verse innovation</th>\n",
       "      <td>3</td>\n",
       "      <td>1550000000.00</td>\n",
       "      <td>516666666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dream sports</th>\n",
       "      <td>2</td>\n",
       "      <td>1240000000.00</td>\n",
       "      <td>620000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zomato</th>\n",
       "      <td>7</td>\n",
       "      <td>1239000000.00</td>\n",
       "      <td>177000000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count             sum            mean\n",
       "Company Name                                                       \n",
       "Alteria capital                   1 150000000000.00 150000000000.00\n",
       "Reliance retail ventures ltd      1  70000000000.00  70000000000.00\n",
       "Snowflake                         1   3000000000.00   3000000000.00\n",
       "Byju's                           10   2522000000.00    252200000.00\n",
       "Reliance                          1   2200000000.00   2200000000.00\n",
       "Swiggy                            4   1956000000.00    489000000.00\n",
       "Oyo                               6   1665000000.00    277500000.00\n",
       "Verse innovation                  3   1550000000.00    516666666.67\n",
       "Dream sports                      2   1240000000.00    620000000.00\n",
       "Zomato                            7   1239000000.00    177000000.00"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_wise = complete_set.groupby(by = \"Company Name\").Amount.agg([\"count\", \"sum\", \"mean\"]).sort_values(by = \"sum\",\n",
    "                                                                                                         ascending = False)\n",
    "company_wise.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d12f4c1",
   "metadata": {},
   "source": [
    "Looking at the number of deals, the most favoured startups were Byju's (10), Bharatpe (10) and Zomato (7). On the other hand, when we assess by the amount of funding received, Alteria Capital (USD 150bn), Reliance retail ventures ltd (USD 70bn), and Snowflake (USD 3bn) were the investors' favorites. Byju's, Zomato, and Oyo make the top 10 in both assessments implying that they are well favoured by investors both by number of deals and by amounts received."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080d4289",
   "metadata": {},
   "source": [
    "# 5.0 Conclusions and Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957a1161",
   "metadata": {},
   "source": [
    "### 5.1 the Hypothesis"
   ]
  },
  {
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   "source": [
    "With the above in mind, it is safe to accept the null hypothesis, given that funding to startups over the period was centralized around particular locations (Mumbai and Bangalore) and particular sectors (Fintech and Retail)"
   ]
  },
  {
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   "source": [
    "### 5.2 Advice to the Team"
   ]
  },
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   "id": "dce7b168",
   "metadata": {},
   "source": [
    "- Prioritize the fintech, retail, and education sectors for further studies into the possible establishment of the startup.\n",
    "- Prioritize Bangalore, Mumbai and Gurgaon as the possible locations for the startup.\n",
    "- The likeliest source of funding is debt, which may come with some conditionalities. It is then followed by Series C and Series B. Even so, an average startup gets an average of about USD 1.5m at the Seed round.\n",
    "- Seek alternative sources of funding during the early years, as you target survival and growth, as funding usually comes in after a year or two of establishment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9808102",
   "metadata": {},
   "source": [
    "Thanks for reading. I will be grateful for your comments and recommendations. Please do not hesitate to reach out if you have any questions or identify any areas for improvement.\n",
    "\n",
    "Kind regards,\n",
    "Kwame."
   ]
  }
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