Question2(Boys and Girls).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gmaps\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/suyash/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STUID</th>\n",
       "      <th>State</th>\n",
       "      <th>District</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Category</th>\n",
       "      <th>Same language</th>\n",
       "      <th>Siblings</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>Father edu</th>\n",
       "      <th>...</th>\n",
       "      <th>Watch TV</th>\n",
       "      <th>Read magazine</th>\n",
       "      <th>Read a book</th>\n",
       "      <th>Play games</th>\n",
       "      <th>Help in household</th>\n",
       "      <th>Maths %</th>\n",
       "      <th>Reading %</th>\n",
       "      <th>Science %</th>\n",
       "      <th>Social %</th>\n",
       "      <th>Overall %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11011001001</td>\n",
       "      <td>AP</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11011001002</td>\n",
       "      <td>AP</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>12.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11011001003</td>\n",
       "      <td>AP</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>27.78</td>\n",
       "      <td>70.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11011001004</td>\n",
       "      <td>AP</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.00</td>\n",
       "      <td>46.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11011001005</td>\n",
       "      <td>AP</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.55</td>\n",
       "      <td>8.33</td>\n",
       "      <td>11.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         STUID State  District  Gender  Age  Category  Same language  \\\n",
       "0  11011001001    AP         1       1    3         3              1   \n",
       "1  11011001002    AP         1       2    3         4              2   \n",
       "2  11011001003    AP         1       2    3         4              2   \n",
       "3  11011001004    AP         1       2    3         3              2   \n",
       "4  11011001005    AP         1       2    3         3              2   \n",
       "\n",
       "   Siblings  Handicap  Father edu    ...      Watch TV  Read magazine  \\\n",
       "0         5         2           1    ...             3              4   \n",
       "1         5         2           2    ...             4              4   \n",
       "2         5         2           1    ...             4              3   \n",
       "3         4         2           1    ...             4              3   \n",
       "4         5         2           1    ...             2              3   \n",
       "\n",
       "   Read a book  Play games  Help in household Maths %  Reading %  Science %  \\\n",
       "0            3           4                  4   20.37        NaN      27.78   \n",
       "1            3           4                  4   12.96        NaN      38.18   \n",
       "2            3           4                  4   27.78      70.00        NaN   \n",
       "3            3           4                  4     NaN      56.67        NaN   \n",
       "4            3           4                  4     NaN        NaN      14.55   \n",
       "\n",
       "   Social %  Overall %  \n",
       "0       NaN     24.075  \n",
       "1       NaN     25.570  \n",
       "2       NaN     48.890  \n",
       "3     36.00     46.335  \n",
       "4      8.33     11.440  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks=pd.read_csv('nas-pupil-marks.csv')\n",
    "labels=pd.read_csv('nas-labels.csv')\n",
    "subjects=['Maths %','Reading %','Science %','Social %']\n",
    "overall=np.array(marks[subjects])\n",
    "marks['Overall %']=[np.nansum(m)/(4-len(m[np.isnan(m)])) for m in overall]\n",
    "marks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STUID</th>\n",
       "      <th>District</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Category</th>\n",
       "      <th>Same language</th>\n",
       "      <th>Siblings</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>Father edu</th>\n",
       "      <th>Mother edu</th>\n",
       "      <th>...</th>\n",
       "      <th>Watch TV</th>\n",
       "      <th>Read magazine</th>\n",
       "      <th>Read a book</th>\n",
       "      <th>Play games</th>\n",
       "      <th>Help in household</th>\n",
       "      <th>Maths %</th>\n",
       "      <th>Reading %</th>\n",
       "      <th>Science %</th>\n",
       "      <th>Social %</th>\n",
       "      <th>Overall %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.851130e+05</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>185113.000000</td>\n",
       "      <td>92583.000000</td>\n",
       "      <td>93170.000000</td>\n",
       "      <td>90912.000000</td>\n",
       "      <td>89488.000000</td>\n",
       "      <td>180581.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.635035e+10</td>\n",
       "      <td>5.355232</td>\n",
       "      <td>1.518851</td>\n",
       "      <td>3.580602</td>\n",
       "      <td>2.544651</td>\n",
       "      <td>1.270667</td>\n",
       "      <td>3.556892</td>\n",
       "      <td>1.837375</td>\n",
       "      <td>2.059083</td>\n",
       "      <td>1.705634</td>\n",
       "      <td>...</td>\n",
       "      <td>3.412418</td>\n",
       "      <td>2.965545</td>\n",
       "      <td>2.909088</td>\n",
       "      <td>3.326093</td>\n",
       "      <td>3.422564</td>\n",
       "      <td>32.238356</td>\n",
       "      <td>46.220837</td>\n",
       "      <td>36.414321</td>\n",
       "      <td>38.362011</td>\n",
       "      <td>38.098616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.316354e+09</td>\n",
       "      <td>4.166320</td>\n",
       "      <td>0.499646</td>\n",
       "      <td>1.197143</td>\n",
       "      <td>1.234702</td>\n",
       "      <td>0.583097</td>\n",
       "      <td>1.234273</td>\n",
       "      <td>0.480610</td>\n",
       "      <td>1.206983</td>\n",
       "      <td>1.161210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988591</td>\n",
       "      <td>1.176622</td>\n",
       "      <td>1.104659</td>\n",
       "      <td>1.045643</td>\n",
       "      <td>1.130179</td>\n",
       "      <td>16.078786</td>\n",
       "      <td>21.829364</td>\n",
       "      <td>14.901072</td>\n",
       "      <td>15.133622</td>\n",
       "      <td>14.948318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.101100e+10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.807117e+10</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.670000</td>\n",
       "      <td>27.590000</td>\n",
       "      <td>25.930000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>27.035000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.520125e+10</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>27.780000</td>\n",
       "      <td>43.330000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>35.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.504107e+10</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>37.930000</td>\n",
       "      <td>63.330000</td>\n",
       "      <td>44.640000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.502103e+10</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              STUID       District         Gender            Age  \\\n",
       "count  1.851130e+05  185113.000000  185113.000000  185113.000000   \n",
       "mean   2.635035e+10       5.355232       1.518851       3.580602   \n",
       "std    9.316354e+09       4.166320       0.499646       1.197143   \n",
       "min    1.101100e+10       1.000000       1.000000       0.000000   \n",
       "25%    1.807117e+10       2.000000       1.000000       3.000000   \n",
       "50%    2.520125e+10       4.000000       2.000000       4.000000   \n",
       "75%    3.504107e+10       8.000000       2.000000       4.000000   \n",
       "max    4.502103e+10      28.000000       2.000000       6.000000   \n",
       "\n",
       "            Category  Same language       Siblings       Handicap  \\\n",
       "count  185113.000000  185113.000000  185113.000000  185113.000000   \n",
       "mean        2.544651       1.270667       3.556892       1.837375   \n",
       "std         1.234702       0.583097       1.234273       0.480610   \n",
       "min         0.000000       0.000000       1.000000       0.000000   \n",
       "25%         1.000000       1.000000       3.000000       2.000000   \n",
       "50%         3.000000       1.000000       4.000000       2.000000   \n",
       "75%         4.000000       2.000000       5.000000       2.000000   \n",
       "max         4.000000       2.000000       5.000000       2.000000   \n",
       "\n",
       "          Father edu     Mother edu      ...             Watch TV  \\\n",
       "count  185113.000000  185113.000000      ...        185113.000000   \n",
       "mean        2.059083       1.705634      ...             3.412418   \n",
       "std         1.206983       1.161210      ...             0.988591   \n",
       "min         0.000000       0.000000      ...             0.000000   \n",
       "25%         1.000000       1.000000      ...             3.000000   \n",
       "50%         2.000000       2.000000      ...             4.000000   \n",
       "75%         3.000000       2.000000      ...             4.000000   \n",
       "max         5.000000       5.000000      ...             4.000000   \n",
       "\n",
       "       Read magazine    Read a book     Play games  Help in household  \\\n",
       "count  185113.000000  185113.000000  185113.000000      185113.000000   \n",
       "mean        2.965545       2.909088       3.326093           3.422564   \n",
       "std         1.176622       1.104659       1.045643           1.130179   \n",
       "min         0.000000       0.000000       0.000000           0.000000   \n",
       "25%         2.000000       2.000000       3.000000           4.000000   \n",
       "50%         3.000000       3.000000       4.000000           4.000000   \n",
       "75%         4.000000       4.000000       4.000000           4.000000   \n",
       "max         4.000000       4.000000       4.000000           4.000000   \n",
       "\n",
       "            Maths %     Reading %     Science %      Social %      Overall %  \n",
       "count  92583.000000  93170.000000  90912.000000  89488.000000  180581.000000  \n",
       "mean      32.238356     46.220837     36.414321     38.362011      38.098616  \n",
       "std       16.078786     21.829364     14.901072     15.133622      14.948318  \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000  \n",
       "25%       21.670000     27.590000     25.930000     28.000000      27.035000  \n",
       "50%       27.780000     43.330000     33.930000     36.000000      35.645000  \n",
       "75%       37.930000     63.330000     44.640000     48.000000      47.320000  \n",
       "max      100.000000    100.000000    100.000000     96.000000     100.000000  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering only 1 and 2 as value for Gender feature.\n",
    "marks=marks.loc[marks['Gender']!=0,:]\n",
    "marks.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Boys</th>\n",
       "      <th>Girls</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AN</th>\n",
       "      <td>37.290617</td>\n",
       "      <td>38.964184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>34.132947</td>\n",
       "      <td>34.719870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>35.737292</td>\n",
       "      <td>34.848424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>39.810706</td>\n",
       "      <td>37.663836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CG</th>\n",
       "      <td>35.981339</td>\n",
       "      <td>35.600373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Boys      Girls\n",
       "State                      \n",
       "AN     37.290617  38.964184\n",
       "AP     34.132947  34.719870\n",
       "AR     35.737292  34.848424\n",
       "BR     39.810706  37.663836\n",
       "CG     35.981339  35.600373"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ff0017610>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40213f4890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates a Pivot Table, which contains statewise mean of overall marks of boys and girls\n",
    "Overall_Gender=pd.pivot_table(marks,values='Overall %',columns='Gender',index='State',aggfunc=np.nanmean)\n",
    "Overall_Gender.columns=['Boys','Girls']\n",
    "Overall_Gender.head()\n",
    "Overall_Gender['State']=Overall_Gender.index\n",
    "Overall_Gender.plot.bar(figsize=(30,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Boys</th>\n",
       "      <th>Girls</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.290617</td>\n",
       "      <td>38.964184</td>\n",
       "      <td>Andaman &amp; Nicobar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.132947</td>\n",
       "      <td>34.719870</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.737292</td>\n",
       "      <td>34.848424</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39.810706</td>\n",
       "      <td>37.663836</td>\n",
       "      <td>Bihar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.981339</td>\n",
       "      <td>35.600373</td>\n",
       "      <td>Chattisgarh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Boys      Girls              State\n",
       "0  37.290617  38.964184  Andaman & Nicobar\n",
       "1  34.132947  34.719870     Andhra Pradesh\n",
       "2  35.737292  34.848424  Arunachal Pradesh\n",
       "3  39.810706  37.663836              Bihar\n",
       "4  35.981339  35.600373        Chattisgarh"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Overall_Gender=Overall_Gender.merge(labels,left_on='State',right_on='Name',how='inner')\n",
    "Overall_Gender=Overall_Gender[['Boys','Girls','Rename']]\n",
    "Overall_Gender.columns=['Boys','Girls','State']\n",
    "Overall_Gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import googlemaps\n",
    "g=googlemaps.Client(key='AIzaSyD3AaID4KFRI40dKSeN0i9DUsj9zYj6Qkw')\n",
    "pos=[]\n",
    "for i in range(len(Overall_Gender['State'])):\n",
    "    loc=g.geocode(Overall_Gender['State'].iloc[i])\n",
    "    pos.append([loc[0]['geometry']['location']['lat'],loc[0]['geometry']['location']['lng']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "inf=[{'State':Overall_Gender['State'].iloc[i],'Boys':Overall_Gender['Boys'].iloc[i],'Girls':Overall_Gender['Girls'].iloc[i]} for i in range(len(Overall_Gender))]\n",
    "info_box_template = \"\"\"\n",
    "<dl>\n",
    "<dt>State</dt><dd>{State}</dd>\n",
    "<dt>Boys</dt><dd>{Boys}</dd>\n",
    "<dt>Girls</dt><dd>{Girls}</dd>\n",
    "</dl>\n",
    "\"\"\"\n",
    "info = [info_box_template.format(**pop) for pop in inf]\n",
    "fig = gmaps.figure()\n",
    "markers = gmaps.marker_layer(pos,info_box_content=info)\n",
    "fig.add_layer(markers)\n",
    "from ipywidgets.embed import embed_minimal_html\n",
    "embed_minimal_html('BoysGirlsPerformance.html',views=[fig])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Ouput of above cell is captured in an html file named 'BoysGirlsPerformance.html'. Ouput is an interactive map. On clicking a marker it pops up a window which consists of state name, mean of marks of boys and mean of marks of girls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4021308f10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Girls V/S Boys')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ff00bee90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates a bar chart, if bar is on left then girls performed better in that state \n",
    "# and if it is on right then boys performed better in that state\n",
    "Overall_Gender['Diff']=Overall_Gender['Boys']-Overall_Gender['Girls']\n",
    "Overall_Gender.index=Overall_Gender['State']\n",
    "Overall_Gender['Diff'].plot.barh(figsize=(20,30))\n",
    "plt.title('Girls V/S Boys')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}