Covid19 and impact on students.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>ID</th>\n",
       "      <th>Region of residence</th>\n",
       "      <th>Age of Subject</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Rating of Online Class experience</th>\n",
       "      <th>Medium for online class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Prefered social media platform</th>\n",
       "      <th>Time spent on TV</th>\n",
       "      <th>Number of meals per day</th>\n",
       "      <th>Change in your weight</th>\n",
       "      <th>Health issue during lockdown</th>\n",
       "      <th>Stress busters</th>\n",
       "      <th>Time utilized</th>\n",
       "      <th>Do you find yourself more connected with your family, close friends , relatives  ?</th>\n",
       "      <th>What you miss the most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Linkedin</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R2</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Scrolling through social media</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Roaming around freely</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R3</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Linkedin</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R4</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Watching web series</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>Friends , relatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R5</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>R1191</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>R1192</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>14</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Friends , relatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>R1193</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Online gaming</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>R1194</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>14</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Reading books</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>R1195</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Talking</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1182 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Region of residence  Age of Subject  Time spent on Online Class  \\\n",
       "0        R1           Delhi-NCR              21                         2.0   \n",
       "1        R2           Delhi-NCR              21                         0.0   \n",
       "2        R3           Delhi-NCR              20                         7.0   \n",
       "3        R4           Delhi-NCR              20                         3.0   \n",
       "4        R5           Delhi-NCR              21                         3.0   \n",
       "...     ...                 ...             ...                         ...   \n",
       "1177  R1191           Delhi-NCR              12                         3.0   \n",
       "1178  R1192           Delhi-NCR              14                         6.0   \n",
       "1179  R1193           Delhi-NCR              13                         4.0   \n",
       "1180  R1194           Delhi-NCR              14                         5.0   \n",
       "1181  R1195           Delhi-NCR              13                         5.0   \n",
       "\n",
       "     Rating of Online Class experience Medium for online class  \\\n",
       "0                                 Good          Laptop/Desktop   \n",
       "1                            Excellent              Smartphone   \n",
       "2                            Very poor          Laptop/Desktop   \n",
       "3                            Very poor              Smartphone   \n",
       "4                                 Good          Laptop/Desktop   \n",
       "...                                ...                     ...   \n",
       "1177                              Good              Smartphone   \n",
       "1178                           Average              Smartphone   \n",
       "1179                           Average              Smartphone   \n",
       "1180                         Excellent          Laptop/Desktop   \n",
       "1181                              Good                  Tablet   \n",
       "\n",
       "      Time spent on self study  Time spent on fitness  Time spent on sleep  \\\n",
       "0                          4.0                    0.0                  7.0   \n",
       "1                          0.0                    2.0                 10.0   \n",
       "2                          3.0                    0.0                  6.0   \n",
       "3                          2.0                    1.0                  6.0   \n",
       "4                          3.0                    1.0                  8.0   \n",
       "...                        ...                    ...                  ...   \n",
       "1177                       4.0                    1.0                  8.0   \n",
       "1178                       4.0                    1.0                  9.0   \n",
       "1179                       0.0                    0.5                  8.0   \n",
       "1180                       3.5                    1.0                  8.0   \n",
       "1181                       2.0                    0.5                  7.0   \n",
       "\n",
       "      Time spent on social media Prefered social media platform  \\\n",
       "0                            3.0                       Linkedin   \n",
       "1                            3.0                        Youtube   \n",
       "2                            2.0                       Linkedin   \n",
       "3                            5.0                      Instagram   \n",
       "4                            3.0                      Instagram   \n",
       "...                          ...                            ...   \n",
       "1177                         1.0                      Instagram   \n",
       "1178                         1.0                       Whatsapp   \n",
       "1179                         3.0                        Youtube   \n",
       "1180                         0.5                        Youtube   \n",
       "1181                         1.0                       Whatsapp   \n",
       "\n",
       "     Time spent on TV  Number of meals per day Change in your weight  \\\n",
       "0                   1                        4             Increased   \n",
       "1                   0                        3             Decreased   \n",
       "2                   0                        3       Remain Constant   \n",
       "3                   0                        3             Decreased   \n",
       "4                   1                        4       Remain Constant   \n",
       "...               ...                      ...                   ...   \n",
       "1177                2                        3             Decreased   \n",
       "1178                1                        4       Remain Constant   \n",
       "1179                2                        4             Decreased   \n",
       "1180                1                        4       Remain Constant   \n",
       "1181                1                        3       Remain Constant   \n",
       "\n",
       "     Health issue during lockdown                  Stress busters  \\\n",
       "0                              NO                         Cooking   \n",
       "1                              NO  Scrolling through social media   \n",
       "2                              NO              Listening to music   \n",
       "3                              NO             Watching web series   \n",
       "4                              NO                    Social Media   \n",
       "...                           ...                             ...   \n",
       "1177                           NO                         Dancing   \n",
       "1178                           NO              Listening to music   \n",
       "1179                           NO                   Online gaming   \n",
       "1180                           NO                   Reading books   \n",
       "1181                           NO                         Talking   \n",
       "\n",
       "     Time utilized  \\\n",
       "0              YES   \n",
       "1              YES   \n",
       "2               NO   \n",
       "3               NO   \n",
       "4               NO   \n",
       "...            ...   \n",
       "1177           YES   \n",
       "1178           YES   \n",
       "1179            NO   \n",
       "1180           YES   \n",
       "1181           YES   \n",
       "\n",
       "     Do you find yourself more connected with your family, close friends , relatives  ?  \\\n",
       "0                                                   YES                                   \n",
       "1                                                    NO                                   \n",
       "2                                                   YES                                   \n",
       "3                                                    NO                                   \n",
       "4                                                    NO                                   \n",
       "...                                                 ...                                   \n",
       "1177                                                YES                                   \n",
       "1178                                                YES                                   \n",
       "1179                                                YES                                   \n",
       "1180                                                YES                                   \n",
       "1181                                                YES                                   \n",
       "\n",
       "     What you miss the most  \n",
       "0            School/college  \n",
       "1     Roaming around freely  \n",
       "2                Travelling  \n",
       "3       Friends , relatives  \n",
       "4                Travelling  \n",
       "...                     ...  \n",
       "1177             Travelling  \n",
       "1178    Friends , relatives  \n",
       "1179         School/college  \n",
       "1180         School/college  \n",
       "1181         School/college  \n",
       "\n",
       "[1182 rows x 19 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('COVID-19 Survey Student Responses.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(plt.style.available)\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1182 entries, 0 to 1181\n",
      "Data columns (total 19 columns):\n",
      " #   Column                                                                              Non-Null Count  Dtype  \n",
      "---  ------                                                                              --------------  -----  \n",
      " 0   ID                                                                                  1182 non-null   object \n",
      " 1   Region of residence                                                                 1182 non-null   object \n",
      " 2   Age of Subject                                                                      1182 non-null   int64  \n",
      " 3   Time spent on Online Class                                                          1182 non-null   float64\n",
      " 4   Rating of Online Class experience                                                   1158 non-null   object \n",
      " 5   Medium for online class                                                             1131 non-null   object \n",
      " 6   Time spent on self study                                                            1182 non-null   float64\n",
      " 7   Time spent on fitness                                                               1182 non-null   float64\n",
      " 8   Time spent on sleep                                                                 1182 non-null   float64\n",
      " 9   Time spent on social media                                                          1182 non-null   float64\n",
      " 10  Prefered social media platform                                                      1182 non-null   object \n",
      " 11  Time spent on TV                                                                    1182 non-null   object \n",
      " 12  Number of meals per day                                                             1182 non-null   int64  \n",
      " 13  Change in your weight                                                               1182 non-null   object \n",
      " 14  Health issue during lockdown                                                        1182 non-null   object \n",
      " 15  Stress busters                                                                      1182 non-null   object \n",
      " 16  Time utilized                                                                       1182 non-null   object \n",
      " 17  Do you find yourself more connected with your family, close friends , relatives  ?  1182 non-null   object \n",
      " 18  What you miss the most                                                              1182 non-null   object \n",
      "dtypes: float64(5), int64(2), object(12)\n",
      "memory usage: 175.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Age of Subject</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Number of meals per day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1182.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.165821</td>\n",
       "      <td>3.208841</td>\n",
       "      <td>2.911591</td>\n",
       "      <td>0.765821</td>\n",
       "      <td>7.871235</td>\n",
       "      <td>2.365694</td>\n",
       "      <td>2.917936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.516467</td>\n",
       "      <td>2.101756</td>\n",
       "      <td>2.140590</td>\n",
       "      <td>0.724451</td>\n",
       "      <td>1.615762</td>\n",
       "      <td>1.767336</td>\n",
       "      <td>0.828698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age of Subject  Time spent on Online Class  Time spent on self study  \\\n",
       "count     1182.000000                 1182.000000               1182.000000   \n",
       "mean        20.165821                    3.208841                  2.911591   \n",
       "std          5.516467                    2.101756                  2.140590   \n",
       "min          7.000000                    0.000000                  0.000000   \n",
       "25%         17.000000                    2.000000                  2.000000   \n",
       "50%         20.000000                    3.000000                  2.000000   \n",
       "75%         21.000000                    5.000000                  4.000000   \n",
       "max         59.000000                   10.000000                 18.000000   \n",
       "\n",
       "       Time spent on fitness  Time spent on sleep  Time spent on social media  \\\n",
       "count            1182.000000          1182.000000                 1182.000000   \n",
       "mean                0.765821             7.871235                    2.365694   \n",
       "std                 0.724451             1.615762                    1.767336   \n",
       "min                 0.000000             4.000000                    0.000000   \n",
       "25%                 0.000000             7.000000                    1.000000   \n",
       "50%                 1.000000             8.000000                    2.000000   \n",
       "75%                 1.000000             9.000000                    3.000000   \n",
       "max                 5.000000            15.000000                   10.000000   \n",
       "\n",
       "       Number of meals per day  \n",
       "count              1182.000000  \n",
       "mean                  2.917936  \n",
       "std                   0.828698  \n",
       "min                   1.000000  \n",
       "25%                   2.000000  \n",
       "50%                   3.000000  \n",
       "75%                   3.000000  \n",
       "max                   8.000000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age of Subject</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Number of meals per day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age of Subject</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.171800</td>\n",
       "      <td>0.008600</td>\n",
       "      <td>-0.055098</td>\n",
       "      <td>-0.116710</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>-0.034250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <td>-0.171800</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119318</td>\n",
       "      <td>-0.026745</td>\n",
       "      <td>-0.177782</td>\n",
       "      <td>-0.117121</td>\n",
       "      <td>0.003237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time spent on self study</th>\n",
       "      <td>0.008600</td>\n",
       "      <td>0.119318</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>-0.218573</td>\n",
       "      <td>-0.162613</td>\n",
       "      <td>0.065931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <td>-0.055098</td>\n",
       "      <td>-0.026745</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043975</td>\n",
       "      <td>-0.042855</td>\n",
       "      <td>0.128537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <td>-0.116710</td>\n",
       "      <td>-0.177782</td>\n",
       "      <td>-0.218573</td>\n",
       "      <td>-0.043975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085667</td>\n",
       "      <td>0.046676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time spent on social media</th>\n",
       "      <td>0.069938</td>\n",
       "      <td>-0.117121</td>\n",
       "      <td>-0.162613</td>\n",
       "      <td>-0.042855</td>\n",
       "      <td>0.085667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of meals per day</th>\n",
       "      <td>-0.034250</td>\n",
       "      <td>0.003237</td>\n",
       "      <td>0.065931</td>\n",
       "      <td>0.128537</td>\n",
       "      <td>0.046676</td>\n",
       "      <td>0.029498</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Age of Subject  Time spent on Online Class  \\\n",
       "Age of Subject                    1.000000                   -0.171800   \n",
       "Time spent on Online Class       -0.171800                    1.000000   \n",
       "Time spent on self study          0.008600                    0.119318   \n",
       "Time spent on fitness            -0.055098                   -0.026745   \n",
       "Time spent on sleep              -0.116710                   -0.177782   \n",
       "Time spent on social media        0.069938                   -0.117121   \n",
       "Number of meals per day          -0.034250                    0.003237   \n",
       "\n",
       "                            Time spent on self study  Time spent on fitness  \\\n",
       "Age of Subject                              0.008600              -0.055098   \n",
       "Time spent on Online Class                  0.119318              -0.026745   \n",
       "Time spent on self study                    1.000000               0.022088   \n",
       "Time spent on fitness                       0.022088               1.000000   \n",
       "Time spent on sleep                        -0.218573              -0.043975   \n",
       "Time spent on social media                 -0.162613              -0.042855   \n",
       "Number of meals per day                     0.065931               0.128537   \n",
       "\n",
       "                            Time spent on sleep  Time spent on social media  \\\n",
       "Age of Subject                        -0.116710                    0.069938   \n",
       "Time spent on Online Class            -0.177782                   -0.117121   \n",
       "Time spent on self study              -0.218573                   -0.162613   \n",
       "Time spent on fitness                 -0.043975                   -0.042855   \n",
       "Time spent on sleep                    1.000000                    0.085667   \n",
       "Time spent on social media             0.085667                    1.000000   \n",
       "Number of meals per day                0.046676                    0.029498   \n",
       "\n",
       "                            Number of meals per day  \n",
       "Age of Subject                            -0.034250  \n",
       "Time spent on Online Class                 0.003237  \n",
       "Time spent on self study                   0.065931  \n",
       "Time spent on fitness                      0.128537  \n",
       "Time spent on sleep                        0.046676  \n",
       "Time spent on social media                 0.029498  \n",
       "Number of meals per day                    1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the pairwise correlation of all columns in the dataframe.\n",
    "df.corr()"
   ]
  },
  {
   "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",
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>ID</th>\n",
       "      <th>Region of residence</th>\n",
       "      <th>Student_age</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Rating of Online Class experience</th>\n",
       "      <th>Medium for online class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Prefered social media platform</th>\n",
       "      <th>Time spent on TV</th>\n",
       "      <th>Number of meals per day</th>\n",
       "      <th>Change in your weight</th>\n",
       "      <th>Health issue during lockdown</th>\n",
       "      <th>Stress busters</th>\n",
       "      <th>Time utilized</th>\n",
       "      <th>Do you find yourself more connected with your family, close friends , relatives  ?</th>\n",
       "      <th>What you miss the most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Linkedin</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R2</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Scrolling through social media</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Roaming around freely</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R3</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Linkedin</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R4</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Watching web series</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>Friends , relatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R5</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>R1191</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>R1192</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>14</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Friends , relatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>R1193</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Online gaming</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>R1194</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>14</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Reading books</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>R1195</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Talking</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1182 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Region of residence  Student_age  Time spent on Online Class  \\\n",
       "0        R1           Delhi-NCR           21                         2.0   \n",
       "1        R2           Delhi-NCR           21                         0.0   \n",
       "2        R3           Delhi-NCR           20                         7.0   \n",
       "3        R4           Delhi-NCR           20                         3.0   \n",
       "4        R5           Delhi-NCR           21                         3.0   \n",
       "...     ...                 ...          ...                         ...   \n",
       "1177  R1191           Delhi-NCR           12                         3.0   \n",
       "1178  R1192           Delhi-NCR           14                         6.0   \n",
       "1179  R1193           Delhi-NCR           13                         4.0   \n",
       "1180  R1194           Delhi-NCR           14                         5.0   \n",
       "1181  R1195           Delhi-NCR           13                         5.0   \n",
       "\n",
       "     Rating of Online Class experience Medium for online class  \\\n",
       "0                                 Good          Laptop/Desktop   \n",
       "1                            Excellent              Smartphone   \n",
       "2                            Very poor          Laptop/Desktop   \n",
       "3                            Very poor              Smartphone   \n",
       "4                                 Good          Laptop/Desktop   \n",
       "...                                ...                     ...   \n",
       "1177                              Good              Smartphone   \n",
       "1178                           Average              Smartphone   \n",
       "1179                           Average              Smartphone   \n",
       "1180                         Excellent          Laptop/Desktop   \n",
       "1181                              Good                  Tablet   \n",
       "\n",
       "      Time spent on self study  Time spent on fitness  Time spent on sleep  \\\n",
       "0                          4.0                    0.0                  7.0   \n",
       "1                          0.0                    2.0                 10.0   \n",
       "2                          3.0                    0.0                  6.0   \n",
       "3                          2.0                    1.0                  6.0   \n",
       "4                          3.0                    1.0                  8.0   \n",
       "...                        ...                    ...                  ...   \n",
       "1177                       4.0                    1.0                  8.0   \n",
       "1178                       4.0                    1.0                  9.0   \n",
       "1179                       0.0                    0.5                  8.0   \n",
       "1180                       3.5                    1.0                  8.0   \n",
       "1181                       2.0                    0.5                  7.0   \n",
       "\n",
       "      Time spent on social media Prefered social media platform  \\\n",
       "0                            3.0                       Linkedin   \n",
       "1                            3.0                        Youtube   \n",
       "2                            2.0                       Linkedin   \n",
       "3                            5.0                      Instagram   \n",
       "4                            3.0                      Instagram   \n",
       "...                          ...                            ...   \n",
       "1177                         1.0                      Instagram   \n",
       "1178                         1.0                       Whatsapp   \n",
       "1179                         3.0                        Youtube   \n",
       "1180                         0.5                        Youtube   \n",
       "1181                         1.0                       Whatsapp   \n",
       "\n",
       "     Time spent on TV  Number of meals per day Change in your weight  \\\n",
       "0                   1                        4             Increased   \n",
       "1                   0                        3             Decreased   \n",
       "2                   0                        3       Remain Constant   \n",
       "3                   0                        3             Decreased   \n",
       "4                   1                        4       Remain Constant   \n",
       "...               ...                      ...                   ...   \n",
       "1177                2                        3             Decreased   \n",
       "1178                1                        4       Remain Constant   \n",
       "1179                2                        4             Decreased   \n",
       "1180                1                        4       Remain Constant   \n",
       "1181                1                        3       Remain Constant   \n",
       "\n",
       "     Health issue during lockdown                  Stress busters  \\\n",
       "0                              NO                         Cooking   \n",
       "1                              NO  Scrolling through social media   \n",
       "2                              NO              Listening to music   \n",
       "3                              NO             Watching web series   \n",
       "4                              NO                    Social Media   \n",
       "...                           ...                             ...   \n",
       "1177                           NO                         Dancing   \n",
       "1178                           NO              Listening to music   \n",
       "1179                           NO                   Online gaming   \n",
       "1180                           NO                   Reading books   \n",
       "1181                           NO                         Talking   \n",
       "\n",
       "     Time utilized  \\\n",
       "0              YES   \n",
       "1              YES   \n",
       "2               NO   \n",
       "3               NO   \n",
       "4               NO   \n",
       "...            ...   \n",
       "1177           YES   \n",
       "1178           YES   \n",
       "1179            NO   \n",
       "1180           YES   \n",
       "1181           YES   \n",
       "\n",
       "     Do you find yourself more connected with your family, close friends , relatives  ?  \\\n",
       "0                                                   YES                                   \n",
       "1                                                    NO                                   \n",
       "2                                                   YES                                   \n",
       "3                                                    NO                                   \n",
       "4                                                    NO                                   \n",
       "...                                                 ...                                   \n",
       "1177                                                YES                                   \n",
       "1178                                                YES                                   \n",
       "1179                                                YES                                   \n",
       "1180                                                YES                                   \n",
       "1181                                                YES                                   \n",
       "\n",
       "     What you miss the most  \n",
       "0            School/college  \n",
       "1     Roaming around freely  \n",
       "2                Travelling  \n",
       "3       Friends , relatives  \n",
       "4                Travelling  \n",
       "...                     ...  \n",
       "1177             Travelling  \n",
       "1178    Friends , relatives  \n",
       "1179         School/college  \n",
       "1180         School/college  \n",
       "1181         School/college  \n",
       "\n",
       "[1182 rows x 19 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renaming col to more readable\n",
    "df.rename(columns={'Age of Subject':'Student_age'},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Dataset Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>ID</th>\n",
       "      <th>Region of residence</th>\n",
       "      <th>Student_age</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Rating of Online Class experience</th>\n",
       "      <th>Medium for online class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Prefered social media platform</th>\n",
       "      <th>Time spent on TV</th>\n",
       "      <th>Number of meals per day</th>\n",
       "      <th>Change in your weight</th>\n",
       "      <th>Health issue during lockdown</th>\n",
       "      <th>Stress busters</th>\n",
       "      <th>Time utilized</th>\n",
       "      <th>Do you find yourself more connected with your family, close friends , relatives  ?</th>\n",
       "      <th>What you miss the most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>R33</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Omegle</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Watching orgasm releasing videos</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Nothing this is my usual life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>R35</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Talking to your relatives</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Eating outside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>R35</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Talking to your relatives</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Eating outside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>R40</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Reading books</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>R40</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Reading books</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>R961</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Twitter</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Online surfing</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>Eating outside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>R966</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Scrolling through social media</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Friends , relatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>R966</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Scrolling through social media</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Friends , relatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>R969</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>R969</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID Region of residence  Student_age  Time spent on Online Class  \\\n",
       "32    R33           Delhi-NCR           21                         0.0   \n",
       "34    R35           Delhi-NCR           20                         0.0   \n",
       "34    R35           Delhi-NCR           20                         0.0   \n",
       "39    R40           Delhi-NCR           20                         0.0   \n",
       "39    R40           Delhi-NCR           20                         0.0   \n",
       "..    ...                 ...          ...                         ...   \n",
       "952  R961           Delhi-NCR           20                         0.0   \n",
       "957  R966           Delhi-NCR           21                         0.0   \n",
       "957  R966           Delhi-NCR           21                         0.0   \n",
       "960  R969           Delhi-NCR           23                         0.0   \n",
       "960  R969           Delhi-NCR           23                         0.0   \n",
       "\n",
       "    Rating of Online Class experience Medium for online class  \\\n",
       "32                          Very poor                     NaN   \n",
       "34                                NaN                     NaN   \n",
       "34                                NaN                     NaN   \n",
       "39                                NaN                     NaN   \n",
       "39                                NaN                     NaN   \n",
       "..                                ...                     ...   \n",
       "952                               NaN                     NaN   \n",
       "957                               NaN                     NaN   \n",
       "957                               NaN                     NaN   \n",
       "960                               NaN                     NaN   \n",
       "960                               NaN                     NaN   \n",
       "\n",
       "     Time spent on self study  Time spent on fitness  Time spent on sleep  \\\n",
       "32                        5.0                    2.0                  4.0   \n",
       "34                        6.0                    1.0                  6.0   \n",
       "34                        6.0                    1.0                  6.0   \n",
       "39                        8.0                    0.0                  8.0   \n",
       "39                        8.0                    0.0                  8.0   \n",
       "..                        ...                    ...                  ...   \n",
       "952                       2.0                    0.0                  7.0   \n",
       "957                       6.0                    1.0                  9.0   \n",
       "957                       6.0                    1.0                  9.0   \n",
       "960                       2.0                    2.0                 10.0   \n",
       "960                       2.0                    2.0                 10.0   \n",
       "\n",
       "     Time spent on social media Prefered social media platform  \\\n",
       "32                          2.0                         Omegle   \n",
       "34                          5.0                      Instagram   \n",
       "34                          5.0                      Instagram   \n",
       "39                          0.0                        Youtube   \n",
       "39                          0.0                        Youtube   \n",
       "..                          ...                            ...   \n",
       "952                         0.5                        Twitter   \n",
       "957                         2.0                       Whatsapp   \n",
       "957                         2.0                       Whatsapp   \n",
       "960                         6.0                        Youtube   \n",
       "960                         6.0                        Youtube   \n",
       "\n",
       "    Time spent on TV  Number of meals per day Change in your weight  \\\n",
       "32                 0                        3       Remain Constant   \n",
       "34                 0                        4             Increased   \n",
       "34                 0                        4             Increased   \n",
       "39                 0                        3             Increased   \n",
       "39                 0                        3             Increased   \n",
       "..               ...                      ...                   ...   \n",
       "952                2                        2       Remain Constant   \n",
       "957              0.5                        4             Increased   \n",
       "957              0.5                        4             Increased   \n",
       "960                0                        2       Remain Constant   \n",
       "960                0                        2       Remain Constant   \n",
       "\n",
       "    Health issue during lockdown                    Stress busters  \\\n",
       "32                            NO  Watching orgasm releasing videos   \n",
       "34                            NO         Talking to your relatives   \n",
       "34                            NO         Talking to your relatives   \n",
       "39                            NO                     Reading books   \n",
       "39                            NO                     Reading books   \n",
       "..                           ...                               ...   \n",
       "952                           NO                    Online surfing   \n",
       "957                           NO    Scrolling through social media   \n",
       "957                           NO    Scrolling through social media   \n",
       "960                           NO                Listening to music   \n",
       "960                           NO                Listening to music   \n",
       "\n",
       "    Time utilized  \\\n",
       "32            YES   \n",
       "34            YES   \n",
       "34            YES   \n",
       "39             NO   \n",
       "39             NO   \n",
       "..            ...   \n",
       "952            NO   \n",
       "957           YES   \n",
       "957           YES   \n",
       "960           YES   \n",
       "960           YES   \n",
       "\n",
       "    Do you find yourself more connected with your family, close friends , relatives  ?  \\\n",
       "32                                                  NO                                   \n",
       "34                                                  NO                                   \n",
       "34                                                  NO                                   \n",
       "39                                                 YES                                   \n",
       "39                                                 YES                                   \n",
       "..                                                 ...                                   \n",
       "952                                                YES                                   \n",
       "957                                                YES                                   \n",
       "957                                                YES                                   \n",
       "960                                                 NO                                   \n",
       "960                                                 NO                                   \n",
       "\n",
       "            What you miss the most  \n",
       "32   Nothing this is my usual life  \n",
       "34                  Eating outside  \n",
       "34                  Eating outside  \n",
       "39                      Travelling  \n",
       "39                      Travelling  \n",
       "..                             ...  \n",
       "952                 Eating outside  \n",
       "957            Friends , relatives  \n",
       "957            Friends , relatives  \n",
       "960                 School/college  \n",
       "960                 School/college  \n",
       "\n",
       "[75 rows x 19 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plotting all the null values\n",
    "df[df.isnull().values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Good', 'Excellent', 'Very poor', 'Average', nan, 'Poor'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding unique values in col Rating of  online class experience\n",
    "df['Rating of Online Class experience'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Laptop/Desktop', 'Smartphone', 'Tablet', nan, 'Any Gadget',\n",
       "       'Smartphone or Laptop/Desktop'], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Medium for online class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing Nan with Average\n",
    "df['Rating of Online Class experience'] = df['Rating of Online Class experience'].replace(np.nan, 'Average')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Medium for online class'] = df['Medium for online class'].replace(np.nan, 'Smartphone or Laptop/Desktop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>ID</th>\n",
       "      <th>Region of residence</th>\n",
       "      <th>Student_age</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Rating of Online Class experience</th>\n",
       "      <th>Medium for online class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Prefered social media platform</th>\n",
       "      <th>Time spent on TV</th>\n",
       "      <th>Number of meals per day</th>\n",
       "      <th>Change in your weight</th>\n",
       "      <th>Health issue during lockdown</th>\n",
       "      <th>Stress busters</th>\n",
       "      <th>Time utilized</th>\n",
       "      <th>Do you find yourself more connected with your family, close friends , relatives  ?</th>\n",
       "      <th>What you miss the most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, Region of residence, Student_age, Time spent on Online Class, Rating of Online Class experience, Medium for online class, Time spent on self study, Time spent on fitness, Time spent on sleep, Time spent on social media, Prefered social media platform, Time spent on TV, Number of meals per day, Change in your weight, Health issue during lockdown, Stress busters, Time utilized, Do you find yourself more connected with your family, close friends , relatives  ?, What you miss the most]\n",
       "Index: []"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All the null values has been gone\n",
    "df[df.isnull().values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '0', '3', '0.5', 'n', '2', '4.5', '1.5', 'N', '4', '0.3', '5',\n",
       "       'No tv', '0.1', '0.25', '6', '0.6', '7', '8', '15', ' ', '0.75',\n",
       "       '2.5', '3.5', '0.4'], dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time spent on TV'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Time spent on TV'].replace({'n':'0', 'N':'0', 'No tv':'0', ' ':'0', 0:'0'}, inplace = True)\n",
    "df['Time spent on TV'] = df['Time spent on TV'].astype('float', copy = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.  ,  0.  ,  3.  ,  0.5 ,  2.  ,  4.5 ,  1.5 ,  4.  ,  0.3 ,\n",
       "        5.  ,  0.1 ,  0.25,  6.  ,  0.6 ,  7.  ,  8.  , 15.  ,  0.75,\n",
       "        2.5 ,  3.5 ,  0.4 ])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time spent on TV'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Prefered social media platform'].replace({'None ':'None'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['School/college', 'Roaming around freely', 'Travelling',\n",
       "       'Friends , relatives', 'Eating outside', 'Colleagues', 'Job',\n",
       "       'Nothing this is my usual life', 'Gym', 'All of them',\n",
       "       'Friends and School', 'Friends,Romaing and traveling',\n",
       "       'Only friends', 'Taking kids to park', 'All of the above ',\n",
       "       'All the above', 'Internet', 'Going to the movies', 'Nothing',\n",
       "       'school, relatives and friends', 'All ', 'All', 'all of the above',\n",
       "       'Nothing ', 'Eating outside and friends.', 'All above',\n",
       "       'Nah, this is my usual lifestyle anyway, just being lazy....',\n",
       "       'Family ', 'Football', 'Normal life',\n",
       "       'The idea of being around fun loving people but this time has certainly made us all to reconnect (and fill the gap if any) with our families and relatives so it is fun but certainly we do miss hanging out with friends',\n",
       "       'My normal routine', 'ALL', 'NOTHING', 'Being social ',\n",
       "       'Previous mistakes', 'all', 'nothing', 'Playing', '.',\n",
       "       'I have missed nothing ', 'Travelling & Friends',\n",
       "       'To stay alone. ', 'Family', 'Badminton in court',\n",
       "       'Friends, relatives & travelling', 'everything',\n",
       "       'Friends and roaming around freely', 'Metro',\n",
       "       'School and friends.', 'School and my school friends'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['What you miss the most'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['What you miss the most'] = df['What you miss the most'].replace(['All the above','All of the above ','everything','All above','all of the above','ALL','all','All of the above','all of them','All of them','All '],'All')\n",
    "df['What you miss the most'] = df['What you miss the most'].replace(['NOTHING','Nothing this is my usual life','To stay alone. ','Nothing ','Nah, this is my usual lifestyle anyway, just being lazy....','Normal life','My normal routine','nothing'],'Nothing')\n",
    "df['What you miss the most'] = df['What you miss the most'].replace(['Only friends','Friends , relatives','relatives and friends','Family ','The idea of being around fun loving people but this time has certainly made us all to reconnect (and fill the gap if any) with our families and relatives so it is fun but certainly we do miss hanging out with friends','Family'],'Friends/Relatives/Family')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cooking', 'Scrolling through social media', 'Listening to music',\n",
       "       'Watching web series', 'Social Media',\n",
       "       'Coding and studying for exams', 'Online surfing',\n",
       "       'live stream watching', 'Reading', 'Online gaming',\n",
       "       'Reading books', 'Dancing', 'Talking with friends ',\n",
       "       'Talking to your relatives',\n",
       "       'sketching,reading books,meditation,songs',\n",
       "       'Watching orgasm releasing videos', 'Sleep', 'Crying',\n",
       "       'Many of these', 'Web Series', 'drawing ', 'Meditation', 'Cricket',\n",
       "       'Anime Manga', 'Sleeping', 'Sketching and writing', 'Writing',\n",
       "       'pubg', 'Talking', 'Workout ', 'Whatever want', 'Indoor Games',\n",
       "       'watching movies,reading books,games,listening to music,sleep,dancing',\n",
       "       'gardening cartoon',\n",
       "       'listening to music,reading books and dancing.',\n",
       "       'Dont get distreessed',\n",
       "       ' listening music, motion design, graphic design, sleeping.',\n",
       "       'Business', 'Many among these ', 'Talk with childhood friends.',\n",
       "       'Playing ', 'Exercise', 'Talking to friends',\n",
       "       'Netflix, Friends and Books', 'Driving', 'I run', 'Running',\n",
       "       'Exercising', 'Football', 'singing', 'Cardio',\n",
       "       'I cant de-stress myslef',\n",
       "       'working out and some physical activity',\n",
       "       'Writing my own Comics & novels', 'Gym',\n",
       "       'I have no problem of stress ', 'workout',\n",
       "       'No able to reduce the stress ', 'Sleeping, Online games',\n",
       "       'By engaging in my work.', 'Work', 'Painting,. Sewing', 'Drawing',\n",
       "       'Forming ', 'Workout and listening music',\n",
       "       'Both listining music and scrolling down social media', 'Painting',\n",
       "       'Sketching', 'Sports', 'Drawing and painting and sketching',\n",
       "       'With a friend', 'Reading books, music, exercise',\n",
       "       'Watching ted talks and music and books', 'Calling friends',\n",
       "       'no stress', 'Drawing, painting',\n",
       "       'All reading books watching web series listening to music and talking to friends',\n",
       "       'Do some home related stuff', 'Painting ', 'Youtube',\n",
       "       'Taking with parents',\n",
       "       'Online gaming , surfing and listening to music ',\n",
       "       'I play Rubiks cube',\n",
       "       'Listening to music and reading books both . ',\n",
       "       'Poetry, writing books and novels , listening to music too',\n",
       "       'Watching YouTube '], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Stress busters'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Stress busters'] = df['Stress busters'].replace([['Sleep'],['Scrolling through social media'],['Reading books'],['Talking to your relatives']],\n",
    "                                                    ['Sleeping','Social Media','Reading','Talking'])\n",
    "df['Stress busters'] = df['Stress busters'].replace(['Web Series','Watching web series'],'Watching Web-Series')\n",
    "df['Stress busters'] = df['Stress busters'].replace(['Exercising','Exercise','Gym','Workout ','Cardio','workout','working out and some physical activity'],\n",
    "                                                    'Exercise/Gym')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>ID</th>\n",
       "      <th>Region of residence</th>\n",
       "      <th>Student_age</th>\n",
       "      <th>Time spent on Online Class</th>\n",
       "      <th>Rating of Online Class experience</th>\n",
       "      <th>Medium for online class</th>\n",
       "      <th>Time spent on self study</th>\n",
       "      <th>Time spent on fitness</th>\n",
       "      <th>Time spent on sleep</th>\n",
       "      <th>Time spent on social media</th>\n",
       "      <th>Prefered social media platform</th>\n",
       "      <th>Time spent on TV</th>\n",
       "      <th>Number of meals per day</th>\n",
       "      <th>Change in your weight</th>\n",
       "      <th>Health issue during lockdown</th>\n",
       "      <th>Stress busters</th>\n",
       "      <th>Time utilized</th>\n",
       "      <th>Do you find yourself more connected with your family, close friends , relatives  ?</th>\n",
       "      <th>What you miss the most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Linkedin</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Increased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R2</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>Roaming around freely</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R3</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Linkedin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R4</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Very poor</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Watching Web-Series</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>Friends/Relatives/Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R5</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>R1191</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Travelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>R1192</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>14</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Listening to music</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>Friends/Relatives/Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>R1193</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Decreased</td>\n",
       "      <td>NO</td>\n",
       "      <td>Online gaming</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>R1194</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>14</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Laptop/Desktop</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Youtube</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Reading</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>R1195</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whatsapp</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Remain Constant</td>\n",
       "      <td>NO</td>\n",
       "      <td>Talking</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>School/college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1182 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Region of residence  Student_age  Time spent on Online Class  \\\n",
       "0        R1           Delhi-NCR           21                         2.0   \n",
       "1        R2           Delhi-NCR           21                         0.0   \n",
       "2        R3           Delhi-NCR           20                         7.0   \n",
       "3        R4           Delhi-NCR           20                         3.0   \n",
       "4        R5           Delhi-NCR           21                         3.0   \n",
       "...     ...                 ...          ...                         ...   \n",
       "1177  R1191           Delhi-NCR           12                         3.0   \n",
       "1178  R1192           Delhi-NCR           14                         6.0   \n",
       "1179  R1193           Delhi-NCR           13                         4.0   \n",
       "1180  R1194           Delhi-NCR           14                         5.0   \n",
       "1181  R1195           Delhi-NCR           13                         5.0   \n",
       "\n",
       "     Rating of Online Class experience Medium for online class  \\\n",
       "0                                 Good          Laptop/Desktop   \n",
       "1                            Excellent              Smartphone   \n",
       "2                            Very poor          Laptop/Desktop   \n",
       "3                            Very poor              Smartphone   \n",
       "4                                 Good          Laptop/Desktop   \n",
       "...                                ...                     ...   \n",
       "1177                              Good              Smartphone   \n",
       "1178                           Average              Smartphone   \n",
       "1179                           Average              Smartphone   \n",
       "1180                         Excellent          Laptop/Desktop   \n",
       "1181                              Good                  Tablet   \n",
       "\n",
       "      Time spent on self study  Time spent on fitness  Time spent on sleep  \\\n",
       "0                          4.0                    0.0                  7.0   \n",
       "1                          0.0                    2.0                 10.0   \n",
       "2                          3.0                    0.0                  6.0   \n",
       "3                          2.0                    1.0                  6.0   \n",
       "4                          3.0                    1.0                  8.0   \n",
       "...                        ...                    ...                  ...   \n",
       "1177                       4.0                    1.0                  8.0   \n",
       "1178                       4.0                    1.0                  9.0   \n",
       "1179                       0.0                    0.5                  8.0   \n",
       "1180                       3.5                    1.0                  8.0   \n",
       "1181                       2.0                    0.5                  7.0   \n",
       "\n",
       "      Time spent on social media Prefered social media platform  \\\n",
       "0                            3.0                       Linkedin   \n",
       "1                            3.0                        Youtube   \n",
       "2                            2.0                       Linkedin   \n",
       "3                            5.0                      Instagram   \n",
       "4                            3.0                      Instagram   \n",
       "...                          ...                            ...   \n",
       "1177                         1.0                      Instagram   \n",
       "1178                         1.0                       Whatsapp   \n",
       "1179                         3.0                        Youtube   \n",
       "1180                         0.5                        Youtube   \n",
       "1181                         1.0                       Whatsapp   \n",
       "\n",
       "      Time spent on TV  Number of meals per day Change in your weight  \\\n",
       "0                  1.0                        4             Increased   \n",
       "1                  0.0                        3             Decreased   \n",
       "2                  0.0                        3       Remain Constant   \n",
       "3                  0.0                        3             Decreased   \n",
       "4                  1.0                        4       Remain Constant   \n",
       "...                ...                      ...                   ...   \n",
       "1177               2.0                        3             Decreased   \n",
       "1178               1.0                        4       Remain Constant   \n",
       "1179               2.0                        4             Decreased   \n",
       "1180               1.0                        4       Remain Constant   \n",
       "1181               1.0                        3       Remain Constant   \n",
       "\n",
       "     Health issue during lockdown       Stress busters Time utilized  \\\n",
       "0                              NO              Cooking           YES   \n",
       "1                              NO         Social Media           YES   \n",
       "2                              NO   Listening to music            NO   \n",
       "3                              NO  Watching Web-Series            NO   \n",
       "4                              NO         Social Media            NO   \n",
       "...                           ...                  ...           ...   \n",
       "1177                           NO              Dancing           YES   \n",
       "1178                           NO   Listening to music           YES   \n",
       "1179                           NO        Online gaming            NO   \n",
       "1180                           NO              Reading           YES   \n",
       "1181                           NO              Talking           YES   \n",
       "\n",
       "     Do you find yourself more connected with your family, close friends , relatives  ?  \\\n",
       "0                                                   YES                                   \n",
       "1                                                    NO                                   \n",
       "2                                                   YES                                   \n",
       "3                                                    NO                                   \n",
       "4                                                    NO                                   \n",
       "...                                                 ...                                   \n",
       "1177                                                YES                                   \n",
       "1178                                                YES                                   \n",
       "1179                                                YES                                   \n",
       "1180                                                YES                                   \n",
       "1181                                                YES                                   \n",
       "\n",
       "        What you miss the most  \n",
       "0               School/college  \n",
       "1        Roaming around freely  \n",
       "2                   Travelling  \n",
       "3     Friends/Relatives/Family  \n",
       "4                   Travelling  \n",
       "...                        ...  \n",
       "1177                Travelling  \n",
       "1178  Friends/Relatives/Family  \n",
       "1179            School/college  \n",
       "1180            School/college  \n",
       "1181            School/college  \n",
       "\n",
       "[1182 rows x 19 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AGE DISTRIBUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Student_age\", data=df)\n",
    "plt.xlabel('Age of Subject', weight='bold')\n",
    "plt.ylabel('Number of Subjects', weight='bold')\n",
    "plt.grid('True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REGION OF STUDENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Delhi-NCR', 'Outside Delhi-NCR']\n",
      "[721, 461]\n"
     ]
    }
   ],
   "source": [
    "regions = df['Region of residence'].value_counts().keys().tolist()\n",
    "print(regions)\n",
    "students_in_regions = df['Region of residence'].value_counts().tolist()\n",
    "print(students_in_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(students_in_regions, explode = (0,0.1),labels = regions,shadow=True, autopct=lambda x: f'{x:,.2f}%\\n({x * sum(students_in_regions)/100:.0f})',textprops={'fontsize':18,'weight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DEVICES USED FOR ONLINE CLASSES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Laptop/Desktop', 'Smartphone', 'Smartphone or Laptop/Desktop', 'Tablet', 'Any Gadget']\n",
      "[545, 539, 56, 37, 5]\n"
     ]
    }
   ],
   "source": [
    "devices = df['Medium for online class'].value_counts().keys().tolist()\n",
    "print(devices)\n",
    "devices_used_by_students = df['Medium for online class'].value_counts().tolist()\n",
    "print(devices_used_by_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(devices_used_by_students, explode = (0.01,0.02,0.3,0.4,0.5), labels = devices, shadow=True, autopct=lambda p : f'{p:.2f}%  ({p * sum(devices_used_by_students)/100:,.0f})', textprops={'fontsize':18, 'weight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Very poor', 'Average', 'Good', 'Excellent', 'Poor']\n",
      "[413, 411, 230, 98, 30]\n"
     ]
    }
   ],
   "source": [
    "rating_of_online_class_experience = df['Rating of Online Class experience'].value_counts().keys().tolist()\n",
    "print(rating_of_online_class_experience)\n",
    "count = df['Rating of Online Class experience'].value_counts().tolist()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()    \n",
    " \n",
    "ax.bar(rating_of_online_class_experience,count)\n",
    "plt.title('Rating of Online Class experience',size=20)\n",
    "plt.xlabel('Rating',size = 20)\n",
    "plt.ylabel('# Of Students',size = 20) \n",
    "for i, v in enumerate(count):\n",
    "    ax.text(i-.15, \n",
    "              v+3,\n",
    "              count[i],\n",
    "              style = 'italic',\n",
    "              fontsize=14,\n",
    "              color = 'magenta')\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SOCIAL MEDIA PLATFORMS BY STUDENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Instagram', 'Whatsapp', 'Youtube', 'Linkedin', 'Facebook', 'Twitter', 'None', 'Snapchat', 'Reddit', 'Telegram', 'Talklife', 'Quora', 'Elyment', 'WhatsApp', 'Omegle']\n",
      "[352, 336, 314, 61, 52, 28, 18, 8, 5, 3, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "social_media_platforms = df['Prefered social media platform'].value_counts().keys().tolist()\n",
    "print(social_media_platforms)\n",
    "counts_ = df['Prefered social media platform'].value_counts().tolist()\n",
    "print(counts_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.barplot(x = counts_, y = social_media_platforms, palette = 'Set1')\n",
    "plt.xlabel('No of students', size = 16)\n",
    "for i, v in enumerate(counts_):\n",
    "    ax.text(  v+3,\n",
    "              i-.15,\n",
    "              f'{counts_[i]*100/sum(counts_):.2f}%',\n",
    "              style = 'italic',\n",
    "              fontsize=14,\n",
    "              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WHAT STUDENTS MISS THE MOST?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.countplot(x='What you miss the most', data=df, order=df['What you miss the most'].value_counts().index[:10], palette='Set1')\n",
    "plt.xlabel(\"No of students\", size = 20, weight='bold')\n",
    "plt.ylabel(\"What did they miss?\",size = 14, weight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DO STUDENTS FIND THEMSELVES MORE CONNECTED WITH THEIR FAMILY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[831, 351]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connected = df['Do you find yourself more connected with your family, close friends , relatives  ?'].value_counts().keys().tolist()\n",
    "counts = df['Do you find yourself more connected with your family, close friends , relatives  ?'].value_counts().tolist()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(counts, labels = connected, shadow = True, textprops = {'fontsize':18, 'weight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " ## WEIGHT OF STUDENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = 'Change in your weight', x = 'Number of meals per day', data = df)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}