assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data-science-libraries-assignment-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1:\n",
    "## Task 1: Setup and DataFrame Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (2.1.4)\n",
      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# Install Pandas (if not already installed).\n",
    "! pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Pandas and any other necessary libraries.\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jane</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age\n",
       "0   Ruby   22\n",
       "1   John   34\n",
       "2   Jane   24\n",
       "3  Billy   35"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from a dictionary and from a list of dictionaries.\n",
    "# creating a dictionary\n",
    "dict_1 ={   \n",
    "    'Name' : ['Ruby','John','Jane','Billy'],\n",
    "    'Age' : [22, 34, 24, 35]\n",
    "}\n",
    "# creating DataFrame from dictionary\n",
    "df_1 = pd.DataFrame(dict_1)\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age\n",
       "0  Ruby   22\n",
       "1  John   34"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating list of dictionaries\n",
    "dict_2 = [\n",
    "    {'Name' : 'Ruby','Age':22},\n",
    "    {'Name' : 'John', 'Age' : 34}\n",
    "]\n",
    "# creating DataFrame from list of dictionaries\n",
    "df_2 = pd.DataFrame(dict_2)\n",
    "df_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Email</th>\n",
       "      <th>Country</th>\n",
       "      <th>Salary</th>\n",
       "      <th>DateOfJoining</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>invalid</td>\n",
       "      <td>59.0</td>\n",
       "      <td>ycastro@example.org</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>44694.24</td>\n",
       "      <td>2022-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David Jefferson</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jessica Reyes</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60511.62</td>\n",
       "      <td>2022-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenneth Macdonald</td>\n",
       "      <td>68.0</td>\n",
       "      <td>ewolf@example.com</td>\n",
       "      <td>Italy</td>\n",
       "      <td>84223.29</td>\n",
       "      <td>2022-04-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julie Horne</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>24665.13</td>\n",
       "      <td>2020-06-19</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>95</th>\n",
       "      <td>Garrett Davidson</td>\n",
       "      <td>56.0</td>\n",
       "      <td>gjames@example.com</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>86227.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Elizabeth Ross</td>\n",
       "      <td>52.0</td>\n",
       "      <td>richardlewis@example.net</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>29992.69</td>\n",
       "      <td>2020-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>brian50@example.com</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>90628.22</td>\n",
       "      <td>2020-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Patricia Singh</td>\n",
       "      <td>65.0</td>\n",
       "      <td>lramirez@example.org</td>\n",
       "      <td>Greece</td>\n",
       "      <td>85290.44</td>\n",
       "      <td>2020-03-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Christopher Stokes</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>20289.17</td>\n",
       "      <td>2021-09-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name   Age                     Email            Country  \\\n",
       "0              invalid  59.0       ycastro@example.org        Switzerland   \n",
       "1      David Jefferson  21.0                       NaN              China   \n",
       "2        Jessica Reyes  34.0                       NaN                NaN   \n",
       "3    Kenneth Macdonald  68.0         ewolf@example.com              Italy   \n",
       "4          Julie Horne  78.0                       NaN            Ecuador   \n",
       "..                 ...   ...                       ...                ...   \n",
       "95    Garrett Davidson  56.0        gjames@example.com    Solomon Islands   \n",
       "96      Elizabeth Ross  52.0  richardlewis@example.net  Equatorial Guinea   \n",
       "97                 NaN  72.0       brian50@example.com            Somalia   \n",
       "98      Patricia Singh  65.0      lramirez@example.org             Greece   \n",
       "99  Christopher Stokes  42.0                       NaN       South Africa   \n",
       "\n",
       "      Salary DateOfJoining  \n",
       "0   44694.24    2022-09-29  \n",
       "1        NaN    2021-10-04  \n",
       "2   60511.62    2022-05-24  \n",
       "3   84223.29    2022-04-07  \n",
       "4   24665.13    2020-06-19  \n",
       "..       ...           ...  \n",
       "95  86227.88           NaN  \n",
       "96  29992.69    2020-04-08  \n",
       "97  90628.22    2020-04-11  \n",
       "98  85290.44    2020-03-18  \n",
       "99  20289.17    2021-09-23  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a dataset from a CSV file that is provide called `messed_dataset.csv`\n",
    "df = pd.read_csv('messed_dataset.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Viewing and Inspecting Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Name   Age                Email      Country    Salary  \\\n",
      "0            invalid  59.0  ycastro@example.org  Switzerland  44694.24   \n",
      "1    David Jefferson  21.0                  NaN        China       NaN   \n",
      "2      Jessica Reyes  34.0                  NaN          NaN  60511.62   \n",
      "3  Kenneth Macdonald  68.0    ewolf@example.com        Italy  84223.29   \n",
      "4        Julie Horne  78.0                  NaN      Ecuador  24665.13   \n",
      "\n",
      "  DateOfJoining  \n",
      "0    2022-09-29  \n",
      "1    2021-10-04  \n",
      "2    2022-05-24  \n",
      "3    2022-04-07  \n",
      "4    2020-06-19  \n"
     ]
    }
   ],
   "source": [
    "# Display the first and last few rows of the DataFrame.\n",
    "# priniting first 5 rows\n",
    "print(df.head()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Name   Age                     Email            Country  \\\n",
      "95    Garrett Davidson  56.0        gjames@example.com    Solomon Islands   \n",
      "96      Elizabeth Ross  52.0  richardlewis@example.net  Equatorial Guinea   \n",
      "97                 NaN  72.0       brian50@example.com            Somalia   \n",
      "98      Patricia Singh  65.0      lramirez@example.org             Greece   \n",
      "99  Christopher Stokes  42.0                       NaN       South Africa   \n",
      "\n",
      "      Salary DateOfJoining  \n",
      "95  86227.88           NaN  \n",
      "96  29992.69    2020-04-08  \n",
      "97  90628.22    2020-04-11  \n",
      "98  85290.44    2020-03-18  \n",
      "99  20289.17    2021-09-23  \n"
     ]
    }
   ],
   "source": [
    "# printing last 5 rows\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Name           89 non-null     object\n",
      " 1   Age            97 non-null     object\n",
      " 2   Email          80 non-null     object\n",
      " 3   Country        86 non-null     object\n",
      " 4   Salary         94 non-null     object\n",
      " 5   DateOfJoining  79 non-null     object\n",
      "dtypes: object(6)\n",
      "memory usage: 4.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Get a summary of the DataFrame, including basic statistics and data types.\n",
    "print(df.info()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Email</th>\n",
       "      <th>Country</th>\n",
       "      <th>Salary</th>\n",
       "      <th>DateOfJoining</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>89</td>\n",
       "      <td>97</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>85</td>\n",
       "      <td>53</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>invalid</td>\n",
       "      <td>76.0</td>\n",
       "      <td>ycastro@example.org</td>\n",
       "      <td>Greece</td>\n",
       "      <td>invalid</td>\n",
       "      <td>2022-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name   Age                Email Country   Salary DateOfJoining\n",
       "count        89    97                   80      86       94            79\n",
       "unique       85    53                   80      76       93            79\n",
       "top     invalid  76.0  ycastro@example.org  Greece  invalid    2022-09-29\n",
       "freq          5     6                    1       2        2             1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the DataFrame:  (100, 6)\n",
      "Column Names:\n",
      " Index(['Name', 'Age', 'Email', 'Country', 'Salary', 'DateOfJoining'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Display the shape and column names of the DataFrame.\n",
    "print(\"Shape of the DataFrame: \",df.shape)\n",
    "print(\"Column Names:\\n\",df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Selection and Indexing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Switzerland\n",
       "1                 China\n",
       "2                   NaN\n",
       "3                 Italy\n",
       "4               Ecuador\n",
       "            ...        \n",
       "95      Solomon Islands\n",
       "96    Equatorial Guinea\n",
       "97              Somalia\n",
       "98               Greece\n",
       "99         South Africa\n",
       "Name: Country, Length: 100, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select a single column and multiple columns.\n",
    "# selecting a single column\n",
    "df['Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Country</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Greece</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country   Age\n",
       "0         Switzerland  59.0\n",
       "1               China  21.0\n",
       "2                 NaN  34.0\n",
       "3               Italy  68.0\n",
       "4             Ecuador  78.0\n",
       "..                ...   ...\n",
       "95    Solomon Islands  56.0\n",
       "96  Equatorial Guinea  52.0\n",
       "97            Somalia  72.0\n",
       "98             Greece  65.0\n",
       "99       South Africa  42.0\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting multiple column\n",
    "df[['Country','Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                         invalid\n",
       "Age                             59.0\n",
       "Email            ycastro@example.org\n",
       "Country                  Switzerland\n",
       "Salary                      44694.24\n",
       "DateOfJoining             2022-09-29\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select rows by index and by label.\n",
    "# Select specific rows and columns using loc and iloc\n",
    "# by index\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Email</th>\n",
       "      <th>Country</th>\n",
       "      <th>Salary</th>\n",
       "      <th>DateOfJoining</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>invalid</td>\n",
       "      <td>59.0</td>\n",
       "      <td>ycastro@example.org</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>44694.24</td>\n",
       "      <td>2022-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David Jefferson</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jessica Reyes</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60511.62</td>\n",
       "      <td>2022-05-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name   Age                Email      Country    Salary  \\\n",
       "0          invalid  59.0  ycastro@example.org  Switzerland  44694.24   \n",
       "1  David Jefferson  21.0                  NaN        China       NaN   \n",
       "2    Jessica Reyes  34.0                  NaN          NaN  60511.62   \n",
       "\n",
       "  DateOfJoining  \n",
       "0    2022-09-29  \n",
       "1    2021-10-04  \n",
       "2    2022-05-24  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'David Jefferson'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by lable\n",
    "df.loc[1,'Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Name</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>invalid</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David Jefferson</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jessica Reyes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name      Country\n",
       "0          invalid  Switzerland\n",
       "1  David Jefferson        China\n",
       "2    Jessica Reyes          NaN"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:2,['Name','Country']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Handling Missing Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             11\n",
       "Age               3\n",
       "Email            20\n",
       "Country          14\n",
       "Salary            6\n",
       "DateOfJoining    21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify missing values in the DataFrame.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             0\n",
       "Age              0\n",
       "Email            0\n",
       "Country          0\n",
       "Salary           0\n",
       "DateOfJoining    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with missing values.\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "#checking\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values with a specified value\n",
    "df.fillna(value = {\"Age\": 0}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Data Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age\n",
       "0  Ruby   22\n",
       "1  John   34"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>22</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>34</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age  Salary\n",
       "0  Ruby   22    3000\n",
       "1  John   34    2000"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column to the DataFrame.\n",
    "salary_list=[3000,2000]\n",
    "df_2['Salary']=salary_list\n",
    "df_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>22</td>\n",
       "      <td>3000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>34</td>\n",
       "      <td>2000</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age  Salary  id\n",
       "0  Ruby   22    3000  12\n",
       "1  John   34    2000  22"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[\"id\"]=[12,22]\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>3000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>2000</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Salary  id\n",
       "0  Ruby    3000  12\n",
       "1  John    2000  22"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete a column from the DataFrame.\n",
    "df_2.drop(columns=['Age'],inplace=True)\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>3000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>2000</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Salary  ID\n",
       "0  Ruby    3000  12\n",
       "1  John    2000  22"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns in the DataFrame.\n",
    "df_2.rename(columns={'id':'ID'},inplace=True)\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>6000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>4000</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Salary  ID\n",
       "0  Ruby    6000  12\n",
       "1  John    4000  22"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply a function to a column.\n",
    "# doubling the salary\n",
    "df_2['Salary']=df_2['Salary'].apply(lambda x:x*2)\n",
    "df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6: GroupBy Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peter</td>\n",
       "      <td>35</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Score\n",
       "0   John   24     84\n",
       "1    Ana   25     83\n",
       "2   John   24     95\n",
       "3    Ana   25     75\n",
       "4  Peter   35     82"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ={\"Name\":['John', 'Ana', 'John', 'Ana', 'Peter'],\n",
    "      'Age':[24,25,24,25,35],\n",
    "      'Score':[84,83,95,75,82]\n",
    "      }\n",
    "df_3=pd.DataFrame(data)\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000225887C29D0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the DataFrame by a column and calculate summary statistics.\n",
    "grouped=df_3.groupby('Name')\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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=\"2\" halign=\"left\">Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>79.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <td>89.5</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peter</th>\n",
       "      <td>82.0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Score    \n",
       "       mean min\n",
       "Name           \n",
       "Ana    79.0  75\n",
       "John   89.5  84\n",
       "Peter  82.0  82"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary= grouped.agg({\n",
    "    'Score':['mean','min']\n",
    "    \n",
    "})\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group name: Ana\n",
      "  Name  Age  Score\n",
      "1  Ana   25     83\n",
      "3  Ana   25     75\n",
      "Group name: John\n",
      "   Name  Age  Score\n",
      "0  John   24     84\n",
      "2  John   24     95\n",
      "Group name: Peter\n",
      "    Name  Age  Score\n",
      "4  Peter   35     82\n"
     ]
    }
   ],
   "source": [
    "# Iterate over groups and display the group names and data.\n",
    "for group_name, group_data in grouped:\n",
    "    print(f\"Group name: {group_name}\")\n",
    "    print(group_data)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 7: Merging and Joining DataFrames\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A   B\n",
      "K0  A0  B0\n",
      "K1  A1  B1\n",
      "K2  A2  B2\n",
      "     C   D\n",
      "K0  C0  D1\n",
      "K1  C1  D2\n",
      "K2  C2  D3\n",
      "     B   C\n",
      "K0  B0  C0\n",
      "K1  B1  C1\n",
      "K2  B2  C2\n"
     ]
    }
   ],
   "source": [
    "dfAB=pd.DataFrame({'A':['A0','A1','A2'],\n",
    "                 'B':['B0','B1','B2']},\n",
    "index = ['K0','K1','K2'])\n",
    "dfCD = pd.DataFrame({'C': ['C0', 'C1', 'C2'],\n",
    "                   'D': ['D1', 'D2', 'D3']}, \n",
    "                  index = ['K0', 'K1', 'K2'])\n",
    "\n",
    "dfBC = pd.DataFrame({'B':['B0','B1','B2'],\n",
    "                   'C': ['C0', 'C1', 'C2']},\n",
    "                  index = ['K0', 'K1', 'K2'])\n",
    "\n",
    "print(dfAB)\n",
    "print(dfCD)\n",
    "print(dfBC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C\n",
       "0  A0  B0  C0\n",
       "1  A1  B1  C1\n",
       "2  A2  B2  C2"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge two DataFrames on a common column.\n",
    "merged_df=dfAB.merge(dfBC, on='B', how='inner')\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C   D\n",
       "K0  A0  B0  C0  D1\n",
       "K1  A1  B1  C1  D2\n",
       "K2  A2  B2  C2  D3"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join two DataFrames using their indices.\n",
    "joined_df=dfAB.join(dfCD )\n",
    "joined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 8: Working with Dates and Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peter</td>\n",
       "      <td>35</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Score\n",
       "0   John   24     84\n",
       "1    Ana   25     83\n",
       "2   John   24     95\n",
       "3    Ana   25     75\n",
       "4  Peter   35     82"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Score</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>84</td>\n",
       "      <td>2024-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "      <td>2024-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>2024-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "      <td>2024-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peter</td>\n",
       "      <td>35</td>\n",
       "      <td>82</td>\n",
       "      <td>2024-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Score       Date\n",
       "0   John   24     84 2024-01-01\n",
       "1    Ana   25     83 2024-01-02\n",
       "2   John   24     95 2024-01-03\n",
       "3    Ana   25     75 2024-01-04\n",
       "4  Peter   35     82 2024-01-05"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a datetime index for the DataFrame.\n",
    "df_3['Date'] = pd.date_range(start = '1/1/2024', periods = len(df_3), freq = 'D')\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   Name    5 non-null      object        \n",
      " 1   Age     5 non-null      int64         \n",
      " 2   Score   5 non-null      int64         \n",
      " 3   Date    5 non-null      datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2), object(1)\n",
      "memory usage: 292.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Converting to a datetime\n",
    "df_3['Date'] = pd.to_datetime(df_3['Date'])\n",
    "df_3.info() #checking the datatype\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Score</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>84</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ana</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peter</td>\n",
       "      <td>35</td>\n",
       "      <td>82</td>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Score       Date  Year  Month  Day\n",
       "0   John   24     84 2024-01-01  2024      1    1\n",
       "1    Ana   25     83 2024-01-02  2024      1    2\n",
       "2   John   24     95 2024-01-03  2024      1    3\n",
       "3    Ana   25     75 2024-01-04  2024      1    4\n",
       "4  Peter   35     82 2024-01-05  2024      1    5"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting components of dates\n",
    "df_3['Year'] = df_3['Date'].dt.year\n",
    "df_3['Month'] = df_3['Date'].dt.month\n",
    "df_3['Day'] = df_3['Date'].dt.day\n",
    "df_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 9: Input and Output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Email</th>\n",
       "      <th>Country</th>\n",
       "      <th>Salary</th>\n",
       "      <th>DateOfJoining</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>invalid</td>\n",
       "      <td>59.0</td>\n",
       "      <td>ycastro@example.org</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>44694.24</td>\n",
       "      <td>2022-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David Jefferson</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jessica Reyes</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60511.62</td>\n",
       "      <td>2022-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenneth Macdonald</td>\n",
       "      <td>68.0</td>\n",
       "      <td>ewolf@example.com</td>\n",
       "      <td>Italy</td>\n",
       "      <td>84223.29</td>\n",
       "      <td>2022-04-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julie Horne</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>24665.13</td>\n",
       "      <td>2020-06-19</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>95</th>\n",
       "      <td>Garrett Davidson</td>\n",
       "      <td>56.0</td>\n",
       "      <td>gjames@example.com</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>86227.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Elizabeth Ross</td>\n",
       "      <td>52.0</td>\n",
       "      <td>richardlewis@example.net</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>29992.69</td>\n",
       "      <td>2020-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>brian50@example.com</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>90628.22</td>\n",
       "      <td>2020-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Patricia Singh</td>\n",
       "      <td>65.0</td>\n",
       "      <td>lramirez@example.org</td>\n",
       "      <td>Greece</td>\n",
       "      <td>85290.44</td>\n",
       "      <td>2020-03-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Christopher Stokes</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>20289.17</td>\n",
       "      <td>2021-09-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name   Age                     Email            Country  \\\n",
       "0              invalid  59.0       ycastro@example.org        Switzerland   \n",
       "1      David Jefferson  21.0                       NaN              China   \n",
       "2        Jessica Reyes  34.0                       NaN                NaN   \n",
       "3    Kenneth Macdonald  68.0         ewolf@example.com              Italy   \n",
       "4          Julie Horne  78.0                       NaN            Ecuador   \n",
       "..                 ...   ...                       ...                ...   \n",
       "95    Garrett Davidson  56.0        gjames@example.com    Solomon Islands   \n",
       "96      Elizabeth Ross  52.0  richardlewis@example.net  Equatorial Guinea   \n",
       "97                 NaN  72.0       brian50@example.com            Somalia   \n",
       "98      Patricia Singh  65.0      lramirez@example.org             Greece   \n",
       "99  Christopher Stokes  42.0                       NaN       South Africa   \n",
       "\n",
       "      Salary DateOfJoining  \n",
       "0   44694.24    2022-09-29  \n",
       "1        NaN    2021-10-04  \n",
       "2   60511.62    2022-05-24  \n",
       "3   84223.29    2022-04-07  \n",
       "4   24665.13    2020-06-19  \n",
       "..       ...           ...  \n",
       "95  86227.88           NaN  \n",
       "96  29992.69    2020-04-08  \n",
       "97  90628.22    2020-04-11  \n",
       "98  85290.44    2020-03-18  \n",
       "99  20289.17    2021-09-23  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data from a CSV file into a DataFrame.\n",
    "df = pd.read_csv('messed_dataset.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Write the DataFrame to a CSV file.\n",
    "df.to_csv('Dataframe to CSV.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>OrderDate</th>\n",
       "      <th>Region</th>\n",
       "      <th>Manager</th>\n",
       "      <th>SalesMan</th>\n",
       "      <th>Item</th>\n",
       "      <th>Units</th>\n",
       "      <th>Unit_price</th>\n",
       "      <th>Sale_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>East</td>\n",
       "      <td>Martha</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Television</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>113810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-23</td>\n",
       "      <td>Central</td>\n",
       "      <td>Hermann</td>\n",
       "      <td>Shelli</td>\n",
       "      <td>Home Theater</td>\n",
       "      <td>50.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>Central</td>\n",
       "      <td>Hermann</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Television</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>43128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>Central</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>David</td>\n",
       "      <td>Cell Phone</td>\n",
       "      <td>27.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>6075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>West</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>Stephen</td>\n",
       "      <td>Television</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>67088.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderDate   Region  Manager   SalesMan          Item  Units  Unit_price  \\\n",
       "0 2018-01-06     East   Martha  Alexander    Television   95.0      1198.0   \n",
       "1 2018-01-23  Central  Hermann     Shelli  Home Theater   50.0       500.0   \n",
       "2 2018-02-09  Central  Hermann       Luis    Television   36.0      1198.0   \n",
       "3 2018-02-26  Central  Timothy      David    Cell Phone   27.0       225.0   \n",
       "4 2018-03-15     West  Timothy    Stephen    Television   56.0      1198.0   \n",
       "\n",
       "   Sale_amt  \n",
       "0  113810.0  \n",
       "1   25000.0  \n",
       "2   43128.0  \n",
       "3    6075.0  \n",
       "4   67088.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data from an Excel file into a DataFrame. The data given is `SaleData.xlsl`.\n",
    "data2 = pd.read_excel(r\"SaleData.xlsx\")\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the DataFrame to an Excel file.\n",
    "data2.to_excel('To Excel.xlsx',index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 2: Reference to Friday Class on EDA\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Load the provided dataset into a Pandas DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>OrderDate</th>\n",
       "      <th>Region</th>\n",
       "      <th>Manager</th>\n",
       "      <th>SalesMan</th>\n",
       "      <th>Item</th>\n",
       "      <th>Units</th>\n",
       "      <th>Unit_price</th>\n",
       "      <th>Sale_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>East</td>\n",
       "      <td>Martha</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Television</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>113810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-23</td>\n",
       "      <td>Central</td>\n",
       "      <td>Hermann</td>\n",
       "      <td>Shelli</td>\n",
       "      <td>Home Theater</td>\n",
       "      <td>50.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>Central</td>\n",
       "      <td>Hermann</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Television</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>43128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>Central</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>David</td>\n",
       "      <td>Cell Phone</td>\n",
       "      <td>27.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>6075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>West</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>Stephen</td>\n",
       "      <td>Television</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>67088.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderDate   Region  Manager   SalesMan          Item  Units  Unit_price  \\\n",
       "0 2018-01-06     East   Martha  Alexander    Television   95.0      1198.0   \n",
       "1 2018-01-23  Central  Hermann     Shelli  Home Theater   50.0       500.0   \n",
       "2 2018-02-09  Central  Hermann       Luis    Television   36.0      1198.0   \n",
       "3 2018-02-26  Central  Timothy      David    Cell Phone   27.0       225.0   \n",
       "4 2018-03-15     West  Timothy    Stephen    Television   56.0      1198.0   \n",
       "\n",
       "   Sale_amt  \n",
       "0  113810.0  \n",
       "1   25000.0  \n",
       "2   43128.0  \n",
       "3    6075.0  \n",
       "4   67088.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading dataset into pandas dataframe\n",
    "df = pd.read_excel(r\"SaleData.xlsx\")\n",
    "# checking if the data is loaded\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Perform basic data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderDate     2\n",
       "Region        2\n",
       "Manager       2\n",
       "SalesMan      2\n",
       "Item          2\n",
       "Units         0\n",
       "Unit_price    0\n",
       "Sale_amt      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifying missing value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderDate     0\n",
       "Region        0\n",
       "Manager       0\n",
       "SalesMan      0\n",
       "Item          0\n",
       "Units         0\n",
       "Unit_price    0\n",
       "Sale_amt      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop missing values\n",
    "df.dropna(inplace = True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3: Perform data analysis to answer the provided questions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 8)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 43 entries, 0 to 42\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   OrderDate   43 non-null     datetime64[ns]\n",
      " 1   Region      43 non-null     object        \n",
      " 2   Manager     43 non-null     object        \n",
      " 3   SalesMan    43 non-null     object        \n",
      " 4   Item        43 non-null     object        \n",
      " 5   Units       43 non-null     float64       \n",
      " 6   Unit_price  43 non-null     float64       \n",
      " 7   Sale_amt    43 non-null     float64       \n",
      "dtypes: datetime64[ns](1), float64(3), object(4)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# information about the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>OrderDate</th>\n",
       "      <th>Units</th>\n",
       "      <th>Unit_price</th>\n",
       "      <th>Sale_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2018-12-29 00:00:00</td>\n",
       "      <td>49.325581</td>\n",
       "      <td>581.011628</td>\n",
       "      <td>30364.546512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2018-01-06 00:00:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2018-07-03 12:00:00</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>3978.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018-12-29 00:00:00</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>17100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019-06-25 12:00:00</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>42529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019-12-21 00:00:00</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>113810.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30.078248</td>\n",
       "      <td>442.285830</td>\n",
       "      <td>32424.588323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 OrderDate      Units   Unit_price       Sale_amt\n",
       "count                   43  43.000000    43.000000      43.000000\n",
       "mean   2018-12-29 00:00:00  49.325581   581.011628   30364.546512\n",
       "min    2018-01-06 00:00:00   2.000000    58.500000     250.000000\n",
       "25%    2018-07-03 12:00:00  27.500000   225.000000    3978.000000\n",
       "50%    2018-12-29 00:00:00  53.000000   500.000000   17100.000000\n",
       "75%    2019-06-25 12:00:00  74.500000  1198.000000   42529.000000\n",
       "max    2019-12-21 00:00:00  96.000000  1198.000000  113810.000000\n",
       "std                    NaN  30.078248   442.285830   32424.588323"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics of numerical column\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Region</th>\n",
       "      <th>Manager</th>\n",
       "      <th>SalesMan</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Central</td>\n",
       "      <td>Martha</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Home Theater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Region Manager   SalesMan          Item\n",
       "count        43      43         43            43\n",
       "unique        3       4         11             5\n",
       "top     Central  Martha  Alexander  Home Theater\n",
       "freq         24      14          8            15"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics of categorical columns\n",
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Visualize the results using Matplotlib/Seaborn.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Line graph using matplotlib\n",
    "plt.plot(df['OrderDate'],df['Units'])\n",
    "plt.title(\"Simple Line Chart\")\n",
    "plt.xlabel(\"OrderDate\")\n",
    "plt.ylabel(\"Units\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot using matplotlib\n",
    "plt.scatter(df['Unit_price'],df['Units'])\n",
    "plt.title(\"Simple Scatter Plot\")\n",
    "plt.xlabel(\"Unit Price\")\n",
    "plt.ylabel(\"Units\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a bar plot using seaborn\n",
    "sns.barplot(x=\"Item\",y=\"Units\",data=df)\n",
    "plt.title(\"Bar PLot\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}