Medium Articles Statistical Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91ea9be4",
   "metadata": {},
   "source": [
    "# Statistical Analysis of my Technical Articles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5610750f",
   "metadata": {},
   "source": [
    "I love to write blogs on Python, Data Science/AI, Machine Learning & NLP, etc. After receiving an overwhelming number of fans and views\n",
    "in few of my articles, I decided to carry out an exploratory data analysis of my writer data at [Medium](https://sukanyabag.medium.com/). I write for 2 renowned \n",
    "publications, viz., [Analytics Vidhya](https://medium.com/analytics-vidhya) and [Python in Plain English](https://python.plainenglish.io/), and I wanted to know which publications generates more\n",
    "engagement in my articles, whether fans read all articles or specific ones, how publications impact view/reads, so on and so forth! \n",
    "\n",
    "Hence, I scraped and collected the data from Medium and performed this analysis which gave pretty much awesome insights which I can follow in my next articles to increase my followers and readers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "f2e8f7db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Tue Jan 25 20:45:45 2022\n",
    "\n",
    "@author: sukanya\n",
    "\"\"\"\n",
    "#ignore warnings\n",
    "import warnings\n",
    "\n",
    "def fxn():\n",
    "    warnings.warn(\"deprecated\", DeprecationWarning)\n",
    "    \n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    fxn()\n",
    "    \n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "#import dependencies\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm\n",
    "import scipy.stats as stats\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "97014ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#notebook styling\n",
    "sns.set_style('darkgrid')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "ab0ad85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load scraped data\n",
    "df = pd.read_csv('mywriterstats.csv', encoding='cp1252')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "1d3c4660",
   "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>Title</th>\n",
       "      <th>Publication</th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Views</th>\n",
       "      <th>Reads</th>\n",
       "      <th>Read Ratio</th>\n",
       "      <th>Fans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Complete XGBoost Therapy with Python</td>\n",
       "      <td>Python In Plain English</td>\n",
       "      <td>11</td>\n",
       "      <td>438</td>\n",
       "      <td>98</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Federated Learning-A Beginner's Guide</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Automated Hyperparameter Tuning with Keras Tun...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>7</td>\n",
       "      <td>2200</td>\n",
       "      <td>706</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Text Summarization using BERT, GPT2, XLNet</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>6</td>\n",
       "      <td>14600</td>\n",
       "      <td>6300</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Transformers — Let’s Dive Deeeep!</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>12</td>\n",
       "      <td>829</td>\n",
       "      <td>256</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Handling Imbalanced Data — Intuition to Implem...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>5</td>\n",
       "      <td>1100</td>\n",
       "      <td>543</td>\n",
       "      <td>48</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Which Machine Learning Algorithm Should You Us...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>5</td>\n",
       "      <td>29000</td>\n",
       "      <td>9000</td>\n",
       "      <td>31</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Flasgger —an API playground with Flask and Swa...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>2</td>\n",
       "      <td>6600</td>\n",
       "      <td>3700</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COVID-19 Detector Flask App from Chest X-ray I...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>4</td>\n",
       "      <td>1400</td>\n",
       "      <td>768</td>\n",
       "      <td>54</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Automated, Quick and Powerful EDA with Sweetvi...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>6</td>\n",
       "      <td>694</td>\n",
       "      <td>312</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Can you master Machine Learning if You Suck at...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>434</td>\n",
       "      <td>218</td>\n",
       "      <td>50</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Data Science versus Machine Learning — a brief...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10 Best Android Developer Tool to Get You Star...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Top 15 Python Libraries for Data Science You M...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>8</td>\n",
       "      <td>264</td>\n",
       "      <td>149</td>\n",
       "      <td>56</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>What is Data Science?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>255</td>\n",
       "      <td>114</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title  \\\n",
       "0            The Complete XGBoost Therapy with Python   \n",
       "1               Federated Learning-A Beginner's Guide   \n",
       "2   Automated Hyperparameter Tuning with Keras Tun...   \n",
       "3          Text Summarization using BERT, GPT2, XLNet   \n",
       "4                   Transformers — Let’s Dive Deeeep!   \n",
       "5   Handling Imbalanced Data — Intuition to Implem...   \n",
       "6   Which Machine Learning Algorithm Should You Us...   \n",
       "7   Flasgger —an API playground with Flask and Swa...   \n",
       "8   COVID-19 Detector Flask App from Chest X-ray I...   \n",
       "9   Automated, Quick and Powerful EDA with Sweetvi...   \n",
       "10  Can you master Machine Learning if You Suck at...   \n",
       "11  Data Science versus Machine Learning — a brief...   \n",
       "12  10 Best Android Developer Tool to Get You Star...   \n",
       "13  Top 15 Python Libraries for Data Science You M...   \n",
       "14                              What is Data Science?   \n",
       "\n",
       "                Publication  Read Time  Views  Reads  Read Ratio  Fans  \n",
       "0   Python In Plain English         11    438     98          22    19  \n",
       "1          Analytics Vidhya          7     33     21          64    15  \n",
       "2          Analytics Vidhya          7   2200    706          32    37  \n",
       "3          Analytics Vidhya          6  14600   6300          43    59  \n",
       "4          Analytics Vidhya         12    829    256          31    24  \n",
       "5          Analytics Vidhya          5   1100    543          48    41  \n",
       "6          Analytics Vidhya          5  29000   9000          31   600  \n",
       "7          Analytics Vidhya          2   6600   3700          56    25  \n",
       "8          Analytics Vidhya          4   1400    768          54    24  \n",
       "9          Analytics Vidhya          6    694    312          45    19  \n",
       "10                      NaN          6    434    218          50    18  \n",
       "11                      NaN          4     95     48          51    11  \n",
       "12                      NaN          6     85     43          51     8  \n",
       "13         Analytics Vidhya          8    264    149          56    10  \n",
       "14                      NaN          7    255    114          45    12  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b94370",
   "metadata": {},
   "source": [
    "I have written a total of 15 blogs till now, amongst which 10 are published in Analytics Vidhya, 1 in Python in Plain English (since I recently joined there), and 4 in Medium itself, without any publications(when I just joined Medium and was not yet selected as writer for any publication)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9b3424ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop indexes\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "74af016f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15 entries, 0 to 14\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Title        15 non-null     object\n",
      " 1   Publication  11 non-null     object\n",
      " 2   Read Time    15 non-null     int64 \n",
      " 3   Views        15 non-null     int64 \n",
      " 4   Reads        15 non-null     int64 \n",
      " 5   Read Ratio   15 non-null     int64 \n",
      " 6   Fans         15 non-null     int64 \n",
      "dtypes: int64(5), object(2)\n",
      "memory usage: 968.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "cb90608e",
   "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>Read Time</th>\n",
       "      <th>Views</th>\n",
       "      <th>Reads</th>\n",
       "      <th>Read Ratio</th>\n",
       "      <th>Fans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3868.466667</td>\n",
       "      <td>1485.066667</td>\n",
       "      <td>45.266667</td>\n",
       "      <td>61.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.557901</td>\n",
       "      <td>7941.552663</td>\n",
       "      <td>2711.635813</td>\n",
       "      <td>11.597208</td>\n",
       "      <td>149.603221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>259.500000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>694.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>29000.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Read Time         Views        Reads  Read Ratio        Fans\n",
       "count  15.000000     15.000000    15.000000   15.000000   15.000000\n",
       "mean    6.400000   3868.466667  1485.066667   45.266667   61.466667\n",
       "std     2.557901   7941.552663  2711.635813   11.597208  149.603221\n",
       "min     2.000000     33.000000    21.000000   22.000000    8.000000\n",
       "25%     5.000000    259.500000   106.000000   37.500000   13.500000\n",
       "50%     6.000000    694.000000   256.000000   48.000000   19.000000\n",
       "75%     7.000000   1800.000000   737.000000   52.500000   31.000000\n",
       "max    12.000000  29000.000000  9000.000000   64.000000  600.000000"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#basic article statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb1f0a2",
   "metadata": {},
   "source": [
    "From the description we can see that -\n",
    "* Total count of my articles - 15\n",
    "* Average Read Time - 6.4 mins\n",
    "* Average Fans - ~62\n",
    "* Average Read Ratio - 45.2\n",
    "* Average Views - 3868.4\n",
    "* Max Reads - 9000\n",
    "* Max Views - 29000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "fc5fc3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title          False\n",
       "Publication     True\n",
       "Read Time      False\n",
       "Views          False\n",
       "Reads          False\n",
       "Read Ratio     False\n",
       "Fans           False\n",
       "Fan Ratio      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check missing values\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7797bd",
   "metadata": {},
   "source": [
    "As stated earlier, Publications has True for null-check as 4 of my articles are not published anywhere except Medium itself. For, rest of the columns, there is no null/missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "9eef683b",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Read Time</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Views</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Reads</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Read Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>...</td>\n",
       "      <td>149.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>...</td>\n",
       "      <td>218.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1114.5</td>\n",
       "      <td>...</td>\n",
       "      <td>640.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>16.263456</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.75</td>\n",
       "      <td>42.5</td>\n",
       "      <td>48.25</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.535534</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>...</td>\n",
       "      <td>258.5</td>\n",
       "      <td>312.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>16.263456</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27.75</td>\n",
       "      <td>33.5</td>\n",
       "      <td>39.25</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Read Time                                             Views           \\\n",
       "         count mean       std  min   25%  50%    75%   max count     mean   \n",
       "Fans                                                                        \n",
       "59         1.0  6.0       NaN  6.0  6.00  6.0   6.00   6.0   1.0  14600.0   \n",
       "10         1.0  8.0       NaN  8.0  8.00  8.0   8.00   8.0   1.0    264.0   \n",
       "25         1.0  2.0       NaN  2.0  2.00  2.0   2.00   2.0   1.0   6600.0   \n",
       "12         1.0  7.0       NaN  7.0  7.00  7.0   7.00   7.0   1.0    255.0   \n",
       "18         1.0  6.0       NaN  6.0  6.00  6.0   6.00   6.0   1.0    434.0   \n",
       "11         1.0  4.0       NaN  4.0  4.00  4.0   4.00   4.0   1.0     95.0   \n",
       "600        1.0  5.0       NaN  5.0  5.00  5.0   5.00   5.0   1.0  29000.0   \n",
       "24         2.0  8.0  5.656854  4.0  6.00  8.0  10.00  12.0   2.0   1114.5   \n",
       "19         2.0  8.5  3.535534  6.0  7.25  8.5   9.75  11.0   2.0    566.0   \n",
       "\n",
       "      ...   Reads         Read Ratio                                      \\\n",
       "      ...     75%     max      count  mean        std   min    25%   50%   \n",
       "Fans  ...                                                                  \n",
       "59    ...  6300.0  6300.0        1.0  43.0        NaN  43.0  43.00  43.0   \n",
       "10    ...   149.0   149.0        1.0  56.0        NaN  56.0  56.00  56.0   \n",
       "25    ...  3700.0  3700.0        1.0  56.0        NaN  56.0  56.00  56.0   \n",
       "12    ...   114.0   114.0        1.0  45.0        NaN  45.0  45.00  45.0   \n",
       "18    ...   218.0   218.0        1.0  50.0        NaN  50.0  50.00  50.0   \n",
       "11    ...    48.0    48.0        1.0  51.0        NaN  51.0  51.00  51.0   \n",
       "600   ...  9000.0  9000.0        1.0  31.0        NaN  31.0  31.00  31.0   \n",
       "24    ...   640.0   768.0        2.0  42.5  16.263456  31.0  36.75  42.5   \n",
       "19    ...   258.5   312.0        2.0  33.5  16.263456  22.0  27.75  33.5   \n",
       "\n",
       "                   \n",
       "        75%   max  \n",
       "Fans               \n",
       "59    43.00  43.0  \n",
       "10    56.00  56.0  \n",
       "25    56.00  56.0  \n",
       "12    45.00  45.0  \n",
       "18    50.00  50.0  \n",
       "11    51.00  51.0  \n",
       "600   31.00  31.0  \n",
       "24    48.25  54.0  \n",
       "19    39.25  45.0  \n",
       "\n",
       "[9 rows x 32 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stats wrt fans\n",
    "df.groupby('Fans').describe().sample(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "2c280ba5",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Read Time</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Views</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Read Ratio</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Fans</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Flasgger —an API playground with Flask and Swagger UI</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Science versus Machine Learning — a brief description</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automated, Quick and Powerful EDA with Sweetviz Library !</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text Summarization using BERT, GPT2, XLNet</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Complete XGBoost Therapy with Python</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>What is Data Science?</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Which Machine Learning Algorithm Should You Use By Problem Type?</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Can you master Machine Learning if You Suck at Math?</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Best Android Developer Tool to Get You Started on Android Development</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COVID-19 Detector Flask App from Chest X-ray Images with Keras and Deep Learning</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Federated Learning-A Beginner's Guide</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handling Imbalanced Data — Intuition to Implementation</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top 15 Python Libraries for Data Science You Must Know</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Automated Hyperparameter Tuning with Keras Tuner and TensorFlow 2.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transformers — Let’s Dive Deeeep!</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Read Time                  \\\n",
       "                                                       count  mean std   min   \n",
       "Title                                                                          \n",
       "Flasgger —an API playground with Flask and Swag...       1.0   2.0 NaN   2.0   \n",
       "Data Science versus Machine Learning — a brief ...       1.0   4.0 NaN   4.0   \n",
       "Automated, Quick and Powerful EDA with Sweetviz...       1.0   6.0 NaN   6.0   \n",
       "Text Summarization using BERT, GPT2, XLNet               1.0   6.0 NaN   6.0   \n",
       "The Complete XGBoost Therapy with Python                 1.0  11.0 NaN  11.0   \n",
       "What is Data Science?                                    1.0   7.0 NaN   7.0   \n",
       "Which Machine Learning Algorithm Should You Use...       1.0   5.0 NaN   5.0   \n",
       "Can you master Machine Learning if You Suck at ...       1.0   6.0 NaN   6.0   \n",
       "10 Best Android Developer Tool to Get You Start...       1.0   6.0 NaN   6.0   \n",
       "COVID-19 Detector Flask App from Chest X-ray Im...       1.0   4.0 NaN   4.0   \n",
       "Federated Learning-A Beginner's Guide                    1.0   7.0 NaN   7.0   \n",
       "Handling Imbalanced Data — Intuition to Impleme...       1.0   5.0 NaN   5.0   \n",
       "Top 15 Python Libraries for Data Science You Mu...       1.0   8.0 NaN   8.0   \n",
       "Automated Hyperparameter Tuning with Keras Tune...       1.0   7.0 NaN   7.0   \n",
       "Transformers — Let’s Dive Deeeep!                        1.0  12.0 NaN  12.0   \n",
       "\n",
       "                                                                            \\\n",
       "                                                     25%   50%   75%   max   \n",
       "Title                                                                        \n",
       "Flasgger —an API playground with Flask and Swag...   2.0   2.0   2.0   2.0   \n",
       "Data Science versus Machine Learning — a brief ...   4.0   4.0   4.0   4.0   \n",
       "Automated, Quick and Powerful EDA with Sweetviz...   6.0   6.0   6.0   6.0   \n",
       "Text Summarization using BERT, GPT2, XLNet           6.0   6.0   6.0   6.0   \n",
       "The Complete XGBoost Therapy with Python            11.0  11.0  11.0  11.0   \n",
       "What is Data Science?                                7.0   7.0   7.0   7.0   \n",
       "Which Machine Learning Algorithm Should You Use...   5.0   5.0   5.0   5.0   \n",
       "Can you master Machine Learning if You Suck at ...   6.0   6.0   6.0   6.0   \n",
       "10 Best Android Developer Tool to Get You Start...   6.0   6.0   6.0   6.0   \n",
       "COVID-19 Detector Flask App from Chest X-ray Im...   4.0   4.0   4.0   4.0   \n",
       "Federated Learning-A Beginner's Guide                7.0   7.0   7.0   7.0   \n",
       "Handling Imbalanced Data — Intuition to Impleme...   5.0   5.0   5.0   5.0   \n",
       "Top 15 Python Libraries for Data Science You Mu...   8.0   8.0   8.0   8.0   \n",
       "Automated Hyperparameter Tuning with Keras Tune...   7.0   7.0   7.0   7.0   \n",
       "Transformers — Let’s Dive Deeeep!                   12.0  12.0  12.0  12.0   \n",
       "\n",
       "                                                   Views           ...  \\\n",
       "                                                   count     mean  ...   \n",
       "Title                                                              ...   \n",
       "Flasgger —an API playground with Flask and Swag...   1.0   6600.0  ...   \n",
       "Data Science versus Machine Learning — a brief ...   1.0     95.0  ...   \n",
       "Automated, Quick and Powerful EDA with Sweetviz...   1.0    694.0  ...   \n",
       "Text Summarization using BERT, GPT2, XLNet           1.0  14600.0  ...   \n",
       "The Complete XGBoost Therapy with Python             1.0    438.0  ...   \n",
       "What is Data Science?                                1.0    255.0  ...   \n",
       "Which Machine Learning Algorithm Should You Use...   1.0  29000.0  ...   \n",
       "Can you master Machine Learning if You Suck at ...   1.0    434.0  ...   \n",
       "10 Best Android Developer Tool to Get You Start...   1.0     85.0  ...   \n",
       "COVID-19 Detector Flask App from Chest X-ray Im...   1.0   1400.0  ...   \n",
       "Federated Learning-A Beginner's Guide                1.0     33.0  ...   \n",
       "Handling Imbalanced Data — Intuition to Impleme...   1.0   1100.0  ...   \n",
       "Top 15 Python Libraries for Data Science You Mu...   1.0    264.0  ...   \n",
       "Automated Hyperparameter Tuning with Keras Tune...   1.0   2200.0  ...   \n",
       "Transformers — Let’s Dive Deeeep!                    1.0    829.0  ...   \n",
       "\n",
       "                                                   Read Ratio        Fans  \\\n",
       "                                                          75%   max count   \n",
       "Title                                                                       \n",
       "Flasgger —an API playground with Flask and Swag...       56.0  56.0   1.0   \n",
       "Data Science versus Machine Learning — a brief ...       51.0  51.0   1.0   \n",
       "Automated, Quick and Powerful EDA with Sweetviz...       45.0  45.0   1.0   \n",
       "Text Summarization using BERT, GPT2, XLNet               43.0  43.0   1.0   \n",
       "The Complete XGBoost Therapy with Python                 22.0  22.0   1.0   \n",
       "What is Data Science?                                    45.0  45.0   1.0   \n",
       "Which Machine Learning Algorithm Should You Use...       31.0  31.0   1.0   \n",
       "Can you master Machine Learning if You Suck at ...       50.0  50.0   1.0   \n",
       "10 Best Android Developer Tool to Get You Start...       51.0  51.0   1.0   \n",
       "COVID-19 Detector Flask App from Chest X-ray Im...       54.0  54.0   1.0   \n",
       "Federated Learning-A Beginner's Guide                    64.0  64.0   1.0   \n",
       "Handling Imbalanced Data — Intuition to Impleme...       48.0  48.0   1.0   \n",
       "Top 15 Python Libraries for Data Science You Mu...       56.0  56.0   1.0   \n",
       "Automated Hyperparameter Tuning with Keras Tune...       32.0  32.0   1.0   \n",
       "Transformers — Let’s Dive Deeeep!                        31.0  31.0   1.0   \n",
       "\n",
       "                                                                             \\\n",
       "                                                     mean std    min    25%   \n",
       "Title                                                                         \n",
       "Flasgger —an API playground with Flask and Swag...   25.0 NaN   25.0   25.0   \n",
       "Data Science versus Machine Learning — a brief ...   11.0 NaN   11.0   11.0   \n",
       "Automated, Quick and Powerful EDA with Sweetviz...   19.0 NaN   19.0   19.0   \n",
       "Text Summarization using BERT, GPT2, XLNet           59.0 NaN   59.0   59.0   \n",
       "The Complete XGBoost Therapy with Python             19.0 NaN   19.0   19.0   \n",
       "What is Data Science?                                12.0 NaN   12.0   12.0   \n",
       "Which Machine Learning Algorithm Should You Use...  600.0 NaN  600.0  600.0   \n",
       "Can you master Machine Learning if You Suck at ...   18.0 NaN   18.0   18.0   \n",
       "10 Best Android Developer Tool to Get You Start...    8.0 NaN    8.0    8.0   \n",
       "COVID-19 Detector Flask App from Chest X-ray Im...   24.0 NaN   24.0   24.0   \n",
       "Federated Learning-A Beginner's Guide                15.0 NaN   15.0   15.0   \n",
       "Handling Imbalanced Data — Intuition to Impleme...   41.0 NaN   41.0   41.0   \n",
       "Top 15 Python Libraries for Data Science You Mu...   10.0 NaN   10.0   10.0   \n",
       "Automated Hyperparameter Tuning with Keras Tune...   37.0 NaN   37.0   37.0   \n",
       "Transformers — Let’s Dive Deeeep!                    24.0 NaN   24.0   24.0   \n",
       "\n",
       "                                                                         \n",
       "                                                      50%    75%    max  \n",
       "Title                                                                    \n",
       "Flasgger —an API playground with Flask and Swag...   25.0   25.0   25.0  \n",
       "Data Science versus Machine Learning — a brief ...   11.0   11.0   11.0  \n",
       "Automated, Quick and Powerful EDA with Sweetviz...   19.0   19.0   19.0  \n",
       "Text Summarization using BERT, GPT2, XLNet           59.0   59.0   59.0  \n",
       "The Complete XGBoost Therapy with Python             19.0   19.0   19.0  \n",
       "What is Data Science?                                12.0   12.0   12.0  \n",
       "Which Machine Learning Algorithm Should You Use...  600.0  600.0  600.0  \n",
       "Can you master Machine Learning if You Suck at ...   18.0   18.0   18.0  \n",
       "10 Best Android Developer Tool to Get You Start...    8.0    8.0    8.0  \n",
       "COVID-19 Detector Flask App from Chest X-ray Im...   24.0   24.0   24.0  \n",
       "Federated Learning-A Beginner's Guide                15.0   15.0   15.0  \n",
       "Handling Imbalanced Data — Intuition to Impleme...   41.0   41.0   41.0  \n",
       "Top 15 Python Libraries for Data Science You Mu...   10.0   10.0   10.0  \n",
       "Automated Hyperparameter Tuning with Keras Tune...   37.0   37.0   37.0  \n",
       "Transformers — Let’s Dive Deeeep!                    24.0   24.0   24.0  \n",
       "\n",
       "[15 rows x 40 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stats wrt title\n",
    "df.groupby('Title').describe().sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "84a644a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show me the articles which gained max fans (top 3)\n",
    "c = plt.cm.autumn(np.linspace(0,1,10))\n",
    "plt.figure(figsize=(14,6))\n",
    "artticles_w_max_fans = df.sort_values(by='Fans', ascending=False).head(3)\n",
    "#color_palette = ['pink', 'indigo', 'cyan']\n",
    "plt.style.use(['dark_background'])\n",
    "plt.style.use('seaborn-talk')\n",
    "sns.color_palette(\"vlag\", as_cmap=True)\n",
    "plot = sns.barplot(x='Fans', y='Title', data=artticles_w_max_fans, palette= \"dark:salmon_r\", color=c)\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041a7253",
   "metadata": {},
   "source": [
    "So, from the stats wrt title table and the visualization above we can fairly conclude that-\n",
    "\n",
    "* The top 3 articles right now are-\n",
    "    1. Which Machine Learning Algorithm Should You Use By Problem Type?\t\n",
    "    2. Text Summarization using BERT, GPT2, XLNet\t\n",
    "    3. Handling Imbalanced Data — Intuition to Implementation\n",
    "    \n",
    "* The fans I got from the top 3 articles are - \n",
    "    1. Which Machine Learning Algorithm Should You Use By Problem Type?\t- approx. 600 fans\n",
    "    2. Text Summarization using BERT, GPT2, XLNet - approx. 59 fans\n",
    "    3. Handling Imbalanced Data — Intuition to Implementation - approx. 41 fans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "dd5c8167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fans', ylabel='Density'>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the Fans distribution with a histogram and maximum likelihood gaussian distribution fit\n",
    "plt.figure(figsize=(17,6))\n",
    "plt.style.use('seaborn-talk')\n",
    "sns.distplot(df['Fans'], bins = 16, fit=norm, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20afa630",
   "metadata": {},
   "source": [
    "The distplot shows that the rank 1 article (Which Machine Learning Algorithm Should You Use By Problem Type?) made an unbelievable performance that is overshadowing all others. Yes, this blog received 29k views and 9k reads with an overwhelming amount of 9000 fans! No other article stat could even get close to it, hence the graph. However, to show at what density estimate fan counts lie for almost all articles, i used the Kernel Density Estimation (kde = True) which shows that fans are generally lies in the density of 41-57% estimation. \n",
    "\n",
    "Hence, it is proved that the 1st article was EXCEPTIONALLY good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "12dd6a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how effective a post was at turning views into reads == topmost read ratios\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.style.use('seaborn-talk')\n",
    "plt.style.use(['dark_background'])\n",
    "top_read_ratios = df.sort_values(by='Read Ratio', ascending=False).head(16)\n",
    "graph = sns.barplot('Read Ratio', 'Title', data=top_read_ratios)\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f578a5",
   "metadata": {},
   "source": [
    "So, the most effective post that turned almost all viewers into readers is 'Federated Learning-A Beginner's Guide'. Turns out to be very predictable as Federated Learning is a state of the art tech and it is a 'decentralized' ML domain which makes it very attractive to readers. \n",
    "In plain english, what this article made the readers think is - \n",
    "\n",
    "'Wait, what is Federated Learning? I never heard it. Let's open this blog and explore.' - hence it became more effective at turning views to reads. \n",
    "\n",
    "On, the other hand,'The Complete XGBoost Therapy with Python'- the article for which I had to study high-level differential calculus and entire xgboost documentation overnight to explain it to readers, is the least one to gain readers. \n",
    "\n",
    "Why? \n",
    "Cause very few people actually go for the architecture and math🤣! I mean common, we can implement XGBoost in 4 lines, why would they bother the mathematics unless they are nerds like me? 😒\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "ecb0bb7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "377d5e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Read Time', ylabel='Read Ratio'>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Medium estimated read time vs actual read ratio\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.style.use('seaborn-deep')\n",
    "plt.style.use(['dark_background'])\n",
    "sns.barplot(x='Read Time', y='Read Ratio', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f98c017",
   "metadata": {},
   "source": [
    "A very interesting trend I explored here which undoubtedly changed my opinion. While I started writing articles, I used to think that less lengthy the article, more will be the read ratio. As we are rarely patient to go to the main point (as least I am). Turns out to be not true all the time. The articles I wrote with read-time 7mins (pretty-much long and detailed to be obvious, gained read ratio > 60! I must say I gained serious fans and readers!😁\n",
    "\n",
    "![bw](https://c.tenor.com/moJ0WYu8YwIAAAAC/scarlett-johansson-smile.gif \"bw\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db0c8513",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b9dfd2",
   "metadata": {},
   "source": [
    "Well, fans in medium are the people who clapped 👏 your post. In case you know that, you must also know that not all the people who reads the articles are your fans. So creating a new feature 'Fan Ratio' will help me discover how much my article was appreciated!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "c7138021",
   "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>Title</th>\n",
       "      <th>Publication</th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Views</th>\n",
       "      <th>Reads</th>\n",
       "      <th>Read Ratio</th>\n",
       "      <th>Fans</th>\n",
       "      <th>Fan Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Complete XGBoost Therapy with Python</td>\n",
       "      <td>Python In Plain English</td>\n",
       "      <td>11</td>\n",
       "      <td>438</td>\n",
       "      <td>98</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>4.337900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Federated Learning-A Beginner's Guide</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>15</td>\n",
       "      <td>45.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Automated Hyperparameter Tuning with Keras Tun...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>7</td>\n",
       "      <td>2200</td>\n",
       "      <td>706</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>1.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Text Summarization using BERT, GPT2, XLNet</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>6</td>\n",
       "      <td>14600</td>\n",
       "      <td>6300</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>0.404110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Transformers — Let’s Dive Deeeep!</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>12</td>\n",
       "      <td>829</td>\n",
       "      <td>256</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>2.895054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Handling Imbalanced Data — Intuition to Implem...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>5</td>\n",
       "      <td>1100</td>\n",
       "      <td>543</td>\n",
       "      <td>48</td>\n",
       "      <td>41</td>\n",
       "      <td>3.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Which Machine Learning Algorithm Should You Us...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>5</td>\n",
       "      <td>29000</td>\n",
       "      <td>9000</td>\n",
       "      <td>31</td>\n",
       "      <td>600</td>\n",
       "      <td>2.068966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Flasgger —an API playground with Flask and Swa...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>2</td>\n",
       "      <td>6600</td>\n",
       "      <td>3700</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>0.378788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COVID-19 Detector Flask App from Chest X-ray I...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>4</td>\n",
       "      <td>1400</td>\n",
       "      <td>768</td>\n",
       "      <td>54</td>\n",
       "      <td>24</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Automated, Quick and Powerful EDA with Sweetvi...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>6</td>\n",
       "      <td>694</td>\n",
       "      <td>312</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>2.737752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Can you master Machine Learning if You Suck at...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>434</td>\n",
       "      <td>218</td>\n",
       "      <td>50</td>\n",
       "      <td>18</td>\n",
       "      <td>4.147465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Data Science versus Machine Learning — a brief...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>11.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10 Best Android Developer Tool to Get You Star...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>9.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Top 15 Python Libraries for Data Science You M...</td>\n",
       "      <td>Analytics Vidhya</td>\n",
       "      <td>8</td>\n",
       "      <td>264</td>\n",
       "      <td>149</td>\n",
       "      <td>56</td>\n",
       "      <td>10</td>\n",
       "      <td>3.787879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>What is Data Science?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>255</td>\n",
       "      <td>114</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>4.705882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title  \\\n",
       "0            The Complete XGBoost Therapy with Python   \n",
       "1               Federated Learning-A Beginner's Guide   \n",
       "2   Automated Hyperparameter Tuning with Keras Tun...   \n",
       "3          Text Summarization using BERT, GPT2, XLNet   \n",
       "4                   Transformers — Let’s Dive Deeeep!   \n",
       "5   Handling Imbalanced Data — Intuition to Implem...   \n",
       "6   Which Machine Learning Algorithm Should You Us...   \n",
       "7   Flasgger —an API playground with Flask and Swa...   \n",
       "8   COVID-19 Detector Flask App from Chest X-ray I...   \n",
       "9   Automated, Quick and Powerful EDA with Sweetvi...   \n",
       "10  Can you master Machine Learning if You Suck at...   \n",
       "11  Data Science versus Machine Learning — a brief...   \n",
       "12  10 Best Android Developer Tool to Get You Star...   \n",
       "13  Top 15 Python Libraries for Data Science You M...   \n",
       "14                              What is Data Science?   \n",
       "\n",
       "                Publication  Read Time  Views  Reads  Read Ratio  Fans  \\\n",
       "0   Python In Plain English         11    438     98          22    19   \n",
       "1          Analytics Vidhya          7     33     21          64    15   \n",
       "2          Analytics Vidhya          7   2200    706          32    37   \n",
       "3          Analytics Vidhya          6  14600   6300          43    59   \n",
       "4          Analytics Vidhya         12    829    256          31    24   \n",
       "5          Analytics Vidhya          5   1100    543          48    41   \n",
       "6          Analytics Vidhya          5  29000   9000          31   600   \n",
       "7          Analytics Vidhya          2   6600   3700          56    25   \n",
       "8          Analytics Vidhya          4   1400    768          54    24   \n",
       "9          Analytics Vidhya          6    694    312          45    19   \n",
       "10                      NaN          6    434    218          50    18   \n",
       "11                      NaN          4     95     48          51    11   \n",
       "12                      NaN          6     85     43          51     8   \n",
       "13         Analytics Vidhya          8    264    149          56    10   \n",
       "14                      NaN          7    255    114          45    12   \n",
       "\n",
       "    Fan Ratio  \n",
       "0    4.337900  \n",
       "1   45.454545  \n",
       "2    1.681818  \n",
       "3    0.404110  \n",
       "4    2.895054  \n",
       "5    3.727273  \n",
       "6    2.068966  \n",
       "7    0.378788  \n",
       "8    1.714286  \n",
       "9    2.737752  \n",
       "10   4.147465  \n",
       "11  11.578947  \n",
       "12   9.411765  \n",
       "13   3.787879  \n",
       "14   4.705882  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percent of fans actually viewed my article\n",
    "df['Fan Ratio'] = (df['Fans'] / df['Views']) * 100\n",
    "df.sort_values('Fan Ratio', ascending=False)\n",
    "\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d6ecec",
   "metadata": {},
   "source": [
    "Now let's plot Title Vs Fan Ratio to see the results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "f5a79eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.style.use('_classic_test_patch')\n",
    "sorted_fan_ratio = df.sort_values(by='Fan Ratio', ascending=False).head(15)\n",
    "graph_fan_ratio = sns.barplot('Fan Ratio', 'Title', data=sorted_fan_ratio)\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00944029",
   "metadata": {},
   "source": [
    "Nothing surprising as Federated Learning is an emerging tech and hence maximum fans who wait for my articles will click on this one as soon as they see it on their feed!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e99e8c6",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503a4d62",
   "metadata": {},
   "source": [
    "Now comes the most crucial part. To see if publication popularity and standard matters in article engagement. I always get confused after writing a story, when I have to submit it to a publication, it becomes a tough choice, specially when both are good. The following result will now help me decide quickly, by telling me which publication gives me more followers and readers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "3838fbd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Publication\n",
      "Analytics Vidhya           6.485047\n",
      "Python In Plain English    4.337900\n",
      "Name: Fan Ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#publication stats - let's see which pubs gave me more fans\n",
    "fanratio_of_pubs = df.groupby('Publication').mean()['Fan Ratio']\n",
    "fanratio_of_pubs = fanratio_of_pubs.sort_values(ascending=False)\n",
    "print(fanratio_of_pubs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "f2838584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Publication\n",
      "Analytics Vidhya           6.485047\n",
      "Python In Plain English    4.337900\n",
      "Name: Fan Ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#publication stats - let's see which pubs gave me more reads\n",
    "reads_of_pubs = df.groupby('Publication').mean()['Read Ratio']\n",
    "reads_of_pubs = fanratio_of_pubs.sort_values(ascending=False)\n",
    "print(reads_of_pubs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d9f09c",
   "metadata": {},
   "source": [
    "They results are no doubt correct, as Analytics Vidhya is a more renowned and big community especially for data science practitioners and professionals compared to Python In Plain English. Hence, it is subjected to provide more engagement and fans in my articles published there. \n",
    "\n",
    "I also must mention that, I joined Python In Plain English just a few weeks ago and hence only my latest blog is published there. Hence in a data science perspective, my data is biased on articles published at Analytics Vidhya, hence the above results. I will get more clear idea what's going on under the hood, once I have enough articles published on both the publications!\n",
    "\n",
    "The graph visualizing the scores gained, is shown below for better intuition!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "e1446889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# watch publications effect on Fan Ratio\n",
    "plt.figure(figsize=(7,3))\n",
    "sns.barplot(x=fanratio_of_pubs, y=fanratio_of_pubs.index, palette= 'rocket')\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "cd007353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 288x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do fans read posts because they are 'fans' or they actually love it?\n",
    "plt.figure(figsize=(4,3))\n",
    "jplot = sns.jointplot('Read Ratio', 'Fan Ratio', df, kind='reg')\n",
    "#jplot.annotate(stats.pearsonr)\n",
    "plt.show(jplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530a92a8",
   "metadata": {},
   "source": [
    "A very intuitive visualization that speaks for itself! Notice how maximum points lie close to the regression fit line in the shaded region, which clearly proves that my articles are not only having good engagement but also very much likely for reading which creates a strong chance of receiving more fans very soon!\n",
    "\n",
    "A jump from only 6 fans last year to 405 right now clearly proves what the graph just showed!\n",
    "\n",
    "YAY!!! \n",
    "\n",
    "![huehuehue](https://media0.giphy.com/media/HrCPqcP2PMe4Dlb20y/giphy.gif \"huehuehue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "61265690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Coefficient b/w fan ratio and read ratio is:  0.4765250872488176\n",
      "Status - Approximately, 50% correlation found. It's finely correlated. Hence, my articles not only attract fans, but also readers.\n"
     ]
    }
   ],
   "source": [
    "column_1 = df['Read Ratio']\n",
    "column_2 = df['Fan Ratio']\n",
    "pearson_corr_coeff = column_1.corr(column_2)\n",
    "\n",
    "print(\"Pearson Correlation Coefficient b/w fan ratio and read ratio is: \",pearson_corr_coeff)\n",
    "print(\"Status - Approximately, 50% correlation found. It's finely correlated. Hence, my articles not only attract fans, but also readers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1891f144",
   "metadata": {},
   "source": [
    "# Summing it up!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cdbd24",
   "metadata": {},
   "source": [
    "So, let's sum up what critical insights I gained which will be extremely helpful while writing the next article!\n",
    "\n",
    "Top 3 insights gained from the analysis-\n",
    "1. Top 3 artciles till now -\n",
    "1st - Which Machine Learning Algorithm Should You Use By Problem Type?\n",
    "2nd - Text Summarization using BERT, GPT2, XLNet\n",
    "3rd - Handling Imbalanced Data — Intuition to Implementation\n",
    "\n",
    "**Insight - Readers are more likely to be engaged with ML, Data Handling \n",
    "and NLP topics.**\n",
    "\n",
    "2. Publication popularity and followers matters for more engagement. \n",
    "\n",
    "**Insight- Stories published by Analytics Vidhya gained far better engagement and fans, \n",
    "compared to Python in Plain english.**\n",
    "\n",
    "3. Read Ratio and Fan Ratio are strongly correlated. \n",
    "\n",
    "**Insight - Stories that are good gets both fans, existing fan reads and other user reads.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c136cc03",
   "metadata": {},
   "source": [
    "*******************************************************************************************************************************"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}