Histograms&Bar_Charts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Preparing Data for visualization* <a id=\"4\"></a>\n",
    "\n",
    "    Preparation is same. For any doubt see the Line_plot for more better understanding if seeing dataset for first time.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read into a pandas dataframe!\n"
     ]
    }
   ],
   "source": [
    "dt = pd.read_csv('cities_r2.csv')\n",
    "print ('Data read into a pandas dataframe!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>state_code</th>\n",
       "      <th>state_name</th>\n",
       "      <th>dist_code</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>...</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>location</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>3</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>9</td>\n",
       "      <td>145238</td>\n",
       "      <td>76840</td>\n",
       "      <td>68398</td>\n",
       "      <td>15870</td>\n",
       "      <td>8587</td>\n",
       "      <td>7283</td>\n",
       "      <td>...</td>\n",
       "      <td>44972</td>\n",
       "      <td>890</td>\n",
       "      <td>848</td>\n",
       "      <td>79.86</td>\n",
       "      <td>85.49</td>\n",
       "      <td>73.59</td>\n",
       "      <td>30.1452928,74.1993043</td>\n",
       "      <td>16287</td>\n",
       "      <td>8612</td>\n",
       "      <td>7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Achalpur</td>\n",
       "      <td>27</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>7</td>\n",
       "      <td>112293</td>\n",
       "      <td>58256</td>\n",
       "      <td>54037</td>\n",
       "      <td>11810</td>\n",
       "      <td>6186</td>\n",
       "      <td>5624</td>\n",
       "      <td>...</td>\n",
       "      <td>43086</td>\n",
       "      <td>928</td>\n",
       "      <td>909</td>\n",
       "      <td>91.99</td>\n",
       "      <td>94.77</td>\n",
       "      <td>89.00</td>\n",
       "      <td>21.257584,77.5086754</td>\n",
       "      <td>8863</td>\n",
       "      <td>5269</td>\n",
       "      <td>3594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adilabad</td>\n",
       "      <td>28</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>1</td>\n",
       "      <td>117388</td>\n",
       "      <td>59232</td>\n",
       "      <td>58156</td>\n",
       "      <td>13103</td>\n",
       "      <td>6731</td>\n",
       "      <td>6372</td>\n",
       "      <td>...</td>\n",
       "      <td>37660</td>\n",
       "      <td>982</td>\n",
       "      <td>947</td>\n",
       "      <td>80.51</td>\n",
       "      <td>88.18</td>\n",
       "      <td>72.73</td>\n",
       "      <td>19.0809075,79.560344</td>\n",
       "      <td>10565</td>\n",
       "      <td>6797</td>\n",
       "      <td>3768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adityapur</td>\n",
       "      <td>20</td>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>24</td>\n",
       "      <td>173988</td>\n",
       "      <td>91495</td>\n",
       "      <td>82493</td>\n",
       "      <td>23042</td>\n",
       "      <td>12063</td>\n",
       "      <td>10979</td>\n",
       "      <td>...</td>\n",
       "      <td>54515</td>\n",
       "      <td>902</td>\n",
       "      <td>910</td>\n",
       "      <td>83.46</td>\n",
       "      <td>89.98</td>\n",
       "      <td>76.23</td>\n",
       "      <td>22.7834741,86.1576889</td>\n",
       "      <td>19225</td>\n",
       "      <td>12189</td>\n",
       "      <td>7036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adoni</td>\n",
       "      <td>28</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>21</td>\n",
       "      <td>166537</td>\n",
       "      <td>82743</td>\n",
       "      <td>83794</td>\n",
       "      <td>18406</td>\n",
       "      <td>9355</td>\n",
       "      <td>9051</td>\n",
       "      <td>...</td>\n",
       "      <td>45089</td>\n",
       "      <td>1013</td>\n",
       "      <td>968</td>\n",
       "      <td>68.38</td>\n",
       "      <td>76.58</td>\n",
       "      <td>60.33</td>\n",
       "      <td>15.6322227,77.2728368</td>\n",
       "      <td>11902</td>\n",
       "      <td>7871</td>\n",
       "      <td>4031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  name_of_city  state_code      state_name  dist_code  population_total  \\\n",
       "0      Abohar            3          PUNJAB          9            145238   \n",
       "1    Achalpur           27     MAHARASHTRA          7            112293   \n",
       "2    Adilabad           28  ANDHRA PRADESH          1            117388   \n",
       "3   Adityapur           20       JHARKHAND         24            173988   \n",
       "4       Adoni           28  ANDHRA PRADESH         21            166537   \n",
       "\n",
       "   population_male  population_female  0-6_population_total  \\\n",
       "0            76840              68398                 15870   \n",
       "1            58256              54037                 11810   \n",
       "2            59232              58156                 13103   \n",
       "3            91495              82493                 23042   \n",
       "4            82743              83794                 18406   \n",
       "\n",
       "   0-6_population_male  0-6_population_female  ...  literates_female  \\\n",
       "0                 8587                   7283  ...             44972   \n",
       "1                 6186                   5624  ...             43086   \n",
       "2                 6731                   6372  ...             37660   \n",
       "3                12063                  10979  ...             54515   \n",
       "4                 9355                   9051  ...             45089   \n",
       "\n",
       "   sex_ratio  child_sex_ratio  effective_literacy_rate_total  \\\n",
       "0        890              848                          79.86   \n",
       "1        928              909                          91.99   \n",
       "2        982              947                          80.51   \n",
       "3        902              910                          83.46   \n",
       "4       1013              968                          68.38   \n",
       "\n",
       "   effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "0                         85.49                           73.59   \n",
       "1                         94.77                           89.00   \n",
       "2                         88.18                           72.73   \n",
       "3                         89.98                           76.23   \n",
       "4                         76.58                           60.33   \n",
       "\n",
       "                location  total_graduates male_graduates  female_graduates  \n",
       "0  30.1452928,74.1993043            16287           8612              7675  \n",
       "1   21.257584,77.5086754             8863           5269              3594  \n",
       "2   19.0809075,79.560344            10565           6797              3768  \n",
       "3  22.7834741,86.1576889            19225          12189              7036  \n",
       "4  15.6322227,77.2728368            11902           7871              4031  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()\n",
    "# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 493 entries, 0 to 492\n",
      "Data columns (total 22 columns):\n",
      "name_of_city                      493 non-null object\n",
      "state_code                        493 non-null int64\n",
      "state_name                        493 non-null object\n",
      "dist_code                         493 non-null int64\n",
      "population_total                  493 non-null int64\n",
      "population_male                   493 non-null int64\n",
      "population_female                 493 non-null int64\n",
      "0-6_population_total              493 non-null int64\n",
      "0-6_population_male               493 non-null int64\n",
      "0-6_population_female             493 non-null int64\n",
      "literates_total                   493 non-null int64\n",
      "literates_male                    493 non-null int64\n",
      "literates_female                  493 non-null int64\n",
      "sex_ratio                         493 non-null int64\n",
      "child_sex_ratio                   493 non-null int64\n",
      "effective_literacy_rate_total     493 non-null float64\n",
      "effective_literacy_rate_male      493 non-null float64\n",
      "effective_literacy_rate_female    493 non-null float64\n",
      "location                          493 non-null object\n",
      "total_graduates                   493 non-null int64\n",
      "male_graduates                    493 non-null int64\n",
      "female_graduates                  493 non-null int64\n",
      "dtypes: float64(3), int64(16), object(3)\n",
      "memory usage: 84.8+ KB\n"
     ]
    }
   ],
   "source": [
    "dt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "dt.columns.tolist()\n",
    "dt.index.tolist()\n",
    "\n",
    "print (type(dt.columns.tolist()))\n",
    "print (type(dt.index.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>state_name</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>145238</td>\n",
       "      <td>76840</td>\n",
       "      <td>68398</td>\n",
       "      <td>15870</td>\n",
       "      <td>8587</td>\n",
       "      <td>7283</td>\n",
       "      <td>103319</td>\n",
       "      <td>58347</td>\n",
       "      <td>44972</td>\n",
       "      <td>890</td>\n",
       "      <td>848</td>\n",
       "      <td>79.86</td>\n",
       "      <td>85.49</td>\n",
       "      <td>73.59</td>\n",
       "      <td>16287</td>\n",
       "      <td>8612</td>\n",
       "      <td>7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Achalpur</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>112293</td>\n",
       "      <td>58256</td>\n",
       "      <td>54037</td>\n",
       "      <td>11810</td>\n",
       "      <td>6186</td>\n",
       "      <td>5624</td>\n",
       "      <td>92433</td>\n",
       "      <td>49347</td>\n",
       "      <td>43086</td>\n",
       "      <td>928</td>\n",
       "      <td>909</td>\n",
       "      <td>91.99</td>\n",
       "      <td>94.77</td>\n",
       "      <td>89.00</td>\n",
       "      <td>8863</td>\n",
       "      <td>5269</td>\n",
       "      <td>3594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name_of_city   state_name  population_total  population_male  \\\n",
       "0      Abohar        PUNJAB            145238            76840   \n",
       "1    Achalpur   MAHARASHTRA            112293            58256   \n",
       "\n",
       "   population_female  0-6_population_total  0-6_population_male  \\\n",
       "0              68398                 15870                 8587   \n",
       "1              54037                 11810                 6186   \n",
       "\n",
       "   0-6_population_female  literates_total  literates_male  literates_female  \\\n",
       "0                   7283           103319           58347             44972   \n",
       "1                   5624            92433           49347             43086   \n",
       "\n",
       "   sex_ratio  child_sex_ratio  effective_literacy_rate_total  \\\n",
       "0        890              848                          79.86   \n",
       "1        928              909                          91.99   \n",
       "\n",
       "   effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "0                         85.49                           73.59   \n",
       "1                         94.77                           89.00   \n",
       "\n",
       "   total_graduates  male_graduates  female_graduates  \n",
       "0            16287            8612              7675  \n",
       "1             8863            5269              3594  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
    "dt.drop(['state_code', 'dist_code', 'location'], axis=1, inplace=True)\n",
    "dt.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name_of_city                      0\n",
       "state_name                        0\n",
       "population_total                  0\n",
       "population_male                   0\n",
       "population_female                 0\n",
       "0-6_population_total              0\n",
       "0-6_population_male               0\n",
       "0-6_population_female             0\n",
       "literates_total                   0\n",
       "literates_male                    0\n",
       "literates_female                  0\n",
       "sex_ratio                         0\n",
       "child_sex_ratio                   0\n",
       "effective_literacy_rate_total     0\n",
       "effective_literacy_rate_male      0\n",
       "effective_literacy_rate_female    0\n",
       "total_graduates                   0\n",
       "male_graduates                    0\n",
       "female_graduates                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_of_city</th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PUNJAB</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>145238</td>\n",
       "      <td>76840</td>\n",
       "      <td>68398</td>\n",
       "      <td>15870</td>\n",
       "      <td>8587</td>\n",
       "      <td>7283</td>\n",
       "      <td>103319</td>\n",
       "      <td>58347</td>\n",
       "      <td>44972</td>\n",
       "      <td>890</td>\n",
       "      <td>848</td>\n",
       "      <td>79.86</td>\n",
       "      <td>85.49</td>\n",
       "      <td>73.59</td>\n",
       "      <td>16287</td>\n",
       "      <td>8612</td>\n",
       "      <td>7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAHARASHTRA</th>\n",
       "      <td>Achalpur</td>\n",
       "      <td>112293</td>\n",
       "      <td>58256</td>\n",
       "      <td>54037</td>\n",
       "      <td>11810</td>\n",
       "      <td>6186</td>\n",
       "      <td>5624</td>\n",
       "      <td>92433</td>\n",
       "      <td>49347</td>\n",
       "      <td>43086</td>\n",
       "      <td>928</td>\n",
       "      <td>909</td>\n",
       "      <td>91.99</td>\n",
       "      <td>94.77</td>\n",
       "      <td>89.00</td>\n",
       "      <td>8863</td>\n",
       "      <td>5269</td>\n",
       "      <td>3594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name_of_city  population_total  population_male  \\\n",
       "state_name                                                    \n",
       "PUNJAB           Abohar             145238            76840   \n",
       "MAHARASHTRA    Achalpur             112293            58256   \n",
       "\n",
       "             population_female  0-6_population_total  0-6_population_male  \\\n",
       "state_name                                                                  \n",
       "PUNJAB                   68398                 15870                 8587   \n",
       "MAHARASHTRA              54037                 11810                 6186   \n",
       "\n",
       "             0-6_population_female  literates_total  literates_male  \\\n",
       "state_name                                                            \n",
       "PUNJAB                        7283           103319           58347   \n",
       "MAHARASHTRA                   5624            92433           49347   \n",
       "\n",
       "             literates_female  sex_ratio  child_sex_ratio  \\\n",
       "state_name                                                  \n",
       "PUNJAB                  44972        890              848   \n",
       "MAHARASHTRA             43086        928              909   \n",
       "\n",
       "             effective_literacy_rate_total  effective_literacy_rate_male  \\\n",
       "state_name                                                                 \n",
       "PUNJAB                               79.86                         85.49   \n",
       "MAHARASHTRA                          91.99                         94.77   \n",
       "\n",
       "             effective_literacy_rate_female  total_graduates  male_graduates  \\\n",
       "state_name                                                                     \n",
       "PUNJAB                                73.59            16287            8612   \n",
       "MAHARASHTRA                           89.00             8863            5269   \n",
       "\n",
       "             female_graduates  \n",
       "state_name                     \n",
       "PUNJAB                   7675  \n",
       "MAHARASHTRA              3594  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.set_index('state_name', inplace=True)\n",
    "# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()\n",
    "dt.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>0-6_population_total</th>\n",
       "      <th>0-6_population_male</th>\n",
       "      <th>0-6_population_female</th>\n",
       "      <th>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "      <th>sex_ratio</th>\n",
       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "      <td>4.930000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.481124e+05</td>\n",
       "      <td>2.343468e+05</td>\n",
       "      <td>2.137656e+05</td>\n",
       "      <td>4.709285e+04</td>\n",
       "      <td>24849.527383</td>\n",
       "      <td>22243.320487</td>\n",
       "      <td>3.461527e+05</td>\n",
       "      <td>1.894384e+05</td>\n",
       "      <td>1.567143e+05</td>\n",
       "      <td>930.294118</td>\n",
       "      <td>902.332657</td>\n",
       "      <td>85.131460</td>\n",
       "      <td>89.920162</td>\n",
       "      <td>79.967181</td>\n",
       "      <td>6.620236e+04</td>\n",
       "      <td>3.771556e+04</td>\n",
       "      <td>2.848680e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.033228e+06</td>\n",
       "      <td>5.487786e+05</td>\n",
       "      <td>4.848622e+05</td>\n",
       "      <td>1.050279e+05</td>\n",
       "      <td>55535.310272</td>\n",
       "      <td>49523.241379</td>\n",
       "      <td>8.220952e+05</td>\n",
       "      <td>4.534753e+05</td>\n",
       "      <td>3.690677e+05</td>\n",
       "      <td>55.849106</td>\n",
       "      <td>49.794689</td>\n",
       "      <td>6.186345</td>\n",
       "      <td>5.377492</td>\n",
       "      <td>7.577825</td>\n",
       "      <td>1.778187e+05</td>\n",
       "      <td>9.849574e+04</td>\n",
       "      <td>7.951556e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000360e+05</td>\n",
       "      <td>5.020100e+04</td>\n",
       "      <td>4.512600e+04</td>\n",
       "      <td>6.547000e+03</td>\n",
       "      <td>3406.000000</td>\n",
       "      <td>3107.000000</td>\n",
       "      <td>5.699800e+04</td>\n",
       "      <td>3.475100e+04</td>\n",
       "      <td>2.224700e+04</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>762.000000</td>\n",
       "      <td>49.510000</td>\n",
       "      <td>52.270000</td>\n",
       "      <td>46.450000</td>\n",
       "      <td>2.532000e+03</td>\n",
       "      <td>1.703000e+03</td>\n",
       "      <td>8.290000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.261420e+05</td>\n",
       "      <td>6.638400e+04</td>\n",
       "      <td>6.041100e+04</td>\n",
       "      <td>1.363900e+04</td>\n",
       "      <td>7221.000000</td>\n",
       "      <td>6457.000000</td>\n",
       "      <td>9.768700e+04</td>\n",
       "      <td>5.357800e+04</td>\n",
       "      <td>4.391400e+04</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>81.750000</td>\n",
       "      <td>87.280000</td>\n",
       "      <td>75.800000</td>\n",
       "      <td>1.527700e+04</td>\n",
       "      <td>9.289000e+03</td>\n",
       "      <td>6.114000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.841330e+05</td>\n",
       "      <td>9.665500e+04</td>\n",
       "      <td>8.776800e+04</td>\n",
       "      <td>1.944000e+04</td>\n",
       "      <td>10342.000000</td>\n",
       "      <td>9172.000000</td>\n",
       "      <td>1.413290e+05</td>\n",
       "      <td>7.590600e+04</td>\n",
       "      <td>6.383600e+04</td>\n",
       "      <td>922.000000</td>\n",
       "      <td>903.000000</td>\n",
       "      <td>85.970000</td>\n",
       "      <td>91.180000</td>\n",
       "      <td>80.920000</td>\n",
       "      <td>2.395900e+04</td>\n",
       "      <td>1.404900e+04</td>\n",
       "      <td>9.558000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.490330e+05</td>\n",
       "      <td>1.750550e+05</td>\n",
       "      <td>1.700260e+05</td>\n",
       "      <td>3.794500e+04</td>\n",
       "      <td>19982.000000</td>\n",
       "      <td>17954.000000</td>\n",
       "      <td>2.679000e+05</td>\n",
       "      <td>1.455480e+05</td>\n",
       "      <td>1.235030e+05</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>942.000000</td>\n",
       "      <td>89.330000</td>\n",
       "      <td>93.400000</td>\n",
       "      <td>85.400000</td>\n",
       "      <td>5.036700e+04</td>\n",
       "      <td>2.787200e+04</td>\n",
       "      <td>2.086600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.247845e+07</td>\n",
       "      <td>6.736815e+06</td>\n",
       "      <td>5.741632e+06</td>\n",
       "      <td>1.209275e+06</td>\n",
       "      <td>647938.000000</td>\n",
       "      <td>561337.000000</td>\n",
       "      <td>1.023759e+07</td>\n",
       "      <td>5.727774e+06</td>\n",
       "      <td>4.509812e+06</td>\n",
       "      <td>1093.000000</td>\n",
       "      <td>1185.000000</td>\n",
       "      <td>98.800000</td>\n",
       "      <td>99.300000</td>\n",
       "      <td>98.310000</td>\n",
       "      <td>2.221137e+06</td>\n",
       "      <td>1.210040e+06</td>\n",
       "      <td>1.011097e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       population_total  population_male  population_female  \\\n",
       "count      4.930000e+02     4.930000e+02       4.930000e+02   \n",
       "mean       4.481124e+05     2.343468e+05       2.137656e+05   \n",
       "std        1.033228e+06     5.487786e+05       4.848622e+05   \n",
       "min        1.000360e+05     5.020100e+04       4.512600e+04   \n",
       "25%        1.261420e+05     6.638400e+04       6.041100e+04   \n",
       "50%        1.841330e+05     9.665500e+04       8.776800e+04   \n",
       "75%        3.490330e+05     1.750550e+05       1.700260e+05   \n",
       "max        1.247845e+07     6.736815e+06       5.741632e+06   \n",
       "\n",
       "       0-6_population_total  0-6_population_male  0-6_population_female  \\\n",
       "count          4.930000e+02           493.000000             493.000000   \n",
       "mean           4.709285e+04         24849.527383           22243.320487   \n",
       "std            1.050279e+05         55535.310272           49523.241379   \n",
       "min            6.547000e+03          3406.000000            3107.000000   \n",
       "25%            1.363900e+04          7221.000000            6457.000000   \n",
       "50%            1.944000e+04         10342.000000            9172.000000   \n",
       "75%            3.794500e+04         19982.000000           17954.000000   \n",
       "max            1.209275e+06        647938.000000          561337.000000   \n",
       "\n",
       "       literates_total  literates_male  literates_female    sex_ratio  \\\n",
       "count     4.930000e+02    4.930000e+02      4.930000e+02   493.000000   \n",
       "mean      3.461527e+05    1.894384e+05      1.567143e+05   930.294118   \n",
       "std       8.220952e+05    4.534753e+05      3.690677e+05    55.849106   \n",
       "min       5.699800e+04    3.475100e+04      2.224700e+04   700.000000   \n",
       "25%       9.768700e+04    5.357800e+04      4.391400e+04   890.000000   \n",
       "50%       1.413290e+05    7.590600e+04      6.383600e+04   922.000000   \n",
       "75%       2.679000e+05    1.455480e+05      1.235030e+05   971.000000   \n",
       "max       1.023759e+07    5.727774e+06      4.509812e+06  1093.000000   \n",
       "\n",
       "       child_sex_ratio  effective_literacy_rate_total  \\\n",
       "count       493.000000                     493.000000   \n",
       "mean        902.332657                      85.131460   \n",
       "std          49.794689                       6.186345   \n",
       "min         762.000000                      49.510000   \n",
       "25%         868.000000                      81.750000   \n",
       "50%         903.000000                      85.970000   \n",
       "75%         942.000000                      89.330000   \n",
       "max        1185.000000                      98.800000   \n",
       "\n",
       "       effective_literacy_rate_male  effective_literacy_rate_female  \\\n",
       "count                    493.000000                      493.000000   \n",
       "mean                      89.920162                       79.967181   \n",
       "std                        5.377492                        7.577825   \n",
       "min                       52.270000                       46.450000   \n",
       "25%                       87.280000                       75.800000   \n",
       "50%                       91.180000                       80.920000   \n",
       "75%                       93.400000                       85.400000   \n",
       "max                       99.300000                       98.310000   \n",
       "\n",
       "       total_graduates  male_graduates  female_graduates  \n",
       "count     4.930000e+02    4.930000e+02      4.930000e+02  \n",
       "mean      6.620236e+04    3.771556e+04      2.848680e+04  \n",
       "std       1.778187e+05    9.849574e+04      7.951556e+04  \n",
       "min       2.532000e+03    1.703000e+03      8.290000e+02  \n",
       "25%       1.527700e+04    9.289000e+03      6.114000e+03  \n",
       "50%       2.395900e+04    1.404900e+04      9.558000e+03  \n",
       "75%       5.036700e+04    2.787200e+04      2.086600e+04  \n",
       "max       2.221137e+06    1.210040e+06      1.011097e+06  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms<a id=\"8\"></a>\n",
    "A histogram is a way of representing the *frequency* distribution of numeric dataset. The way it works is it partitions the x-axis into *bins*, assigns each data point in our dataset to a bin, and then counts the number of data points that have been assigned to each bin. So the y-axis is the frequency or the number of data points in each bin. Note that we can change the bin size and usually one needs to tweak it so that the distribution is displayed nicely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import `Matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the inline backend to generate the plots within the browser\n",
    "%matplotlib inline \n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we proceed with creating the histogram plot, let's first examine the data split into intervals. To do this, we will us **Numpy**'s `histrogram` method to get the bin ranges and frequency counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>population_total</th>\n",
       "      <th>population_male</th>\n",
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       "      <th>0-6_population_male</th>\n",
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       "      <th>child_sex_ratio</th>\n",
       "      <th>effective_literacy_rate_total</th>\n",
       "      <th>effective_literacy_rate_male</th>\n",
       "      <th>effective_literacy_rate_female</th>\n",
       "      <th>total_graduates</th>\n",
       "      <th>male_graduates</th>\n",
       "      <th>female_graduates</th>\n",
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       "    <tr>\n",
       "      <th>state_name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WEST BENGAL</th>\n",
       "      <td>18063509</td>\n",
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       "      <td>57539</td>\n",
       "      <td>57499</td>\n",
       "      <td>5361.31</td>\n",
       "      <td>5570.79</td>\n",
       "      <td>5137.91</td>\n",
       "      <td>2668776</td>\n",
       "      <td>1561629</td>\n",
       "      <td>1107147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTTAR PRADESH</th>\n",
       "      <td>25302925</td>\n",
       "      <td>13433369</td>\n",
       "      <td>11869556</td>\n",
       "      <td>2858869</td>\n",
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       "      <td>9858998</td>\n",
       "      <td>7810410</td>\n",
       "      <td>56151</td>\n",
       "      <td>55073</td>\n",
       "      <td>4995.87</td>\n",
       "      <td>5288.94</td>\n",
       "      <td>4667.93</td>\n",
       "      <td>3692166</td>\n",
       "      <td>2090734</td>\n",
       "      <td>1601432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAHARASHTRA</th>\n",
       "      <td>37722136</td>\n",
       "      <td>19961736</td>\n",
       "      <td>17760400</td>\n",
       "      <td>3930310</td>\n",
       "      <td>2077225</td>\n",
       "      <td>1853085</td>\n",
       "      <td>30514278</td>\n",
       "      <td>16794767</td>\n",
       "      <td>13719511</td>\n",
       "      <td>39582</td>\n",
       "      <td>38014</td>\n",
       "      <td>3862.60</td>\n",
       "      <td>4044.74</td>\n",
       "      <td>3665.93</td>\n",
       "      <td>5266151</td>\n",
       "      <td>2908234</td>\n",
       "      <td>2357917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDHRA PRADESH</th>\n",
       "      <td>18171615</td>\n",
       "      <td>9192368</td>\n",
       "      <td>8979247</td>\n",
       "      <td>1776846</td>\n",
       "      <td>913593</td>\n",
       "      <td>863253</td>\n",
       "      <td>13463769</td>\n",
       "      <td>7178688</td>\n",
       "      <td>6285081</td>\n",
       "      <td>41975</td>\n",
       "      <td>39630</td>\n",
       "      <td>3398.97</td>\n",
       "      <td>3644.80</td>\n",
       "      <td>3154.26</td>\n",
       "      <td>2751351</td>\n",
       "      <td>1668337</td>\n",
       "      <td>1083014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAMIL NADU</th>\n",
       "      <td>13879395</td>\n",
       "      <td>6957261</td>\n",
       "      <td>6922134</td>\n",
       "      <td>1263923</td>\n",
       "      <td>643872</td>\n",
       "      <td>620051</td>\n",
       "      <td>11385700</td>\n",
       "      <td>5920803</td>\n",
       "      <td>5464897</td>\n",
       "      <td>32033</td>\n",
       "      <td>30822</td>\n",
       "      <td>2882.98</td>\n",
       "      <td>3003.35</td>\n",
       "      <td>2763.26</td>\n",
       "      <td>2144970</td>\n",
       "      <td>1182839</td>\n",
       "      <td>962131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population_total  population_male  population_female  \\\n",
       "state_name                                                             \n",
       "WEST BENGAL             18063509          9357777            8705732   \n",
       "UTTAR PRADESH           25302925         13433369           11869556   \n",
       "MAHARASHTRA             37722136         19961736           17760400   \n",
       "ANDHRA PRADESH          18171615          9192368            8979247   \n",
       "TAMIL NADU              13879395          6957261            6922134   \n",
       "\n",
       "                0-6_population_total  0-6_population_male  \\\n",
       "state_name                                                  \n",
       "WEST BENGAL                  1425751               735369   \n",
       "UTTAR PRADESH                2858869              1528442   \n",
       "MAHARASHTRA                  3930310              2077225   \n",
       "ANDHRA PRADESH               1776846               913593   \n",
       "TAMIL NADU                   1263923               643872   \n",
       "\n",
       "                0-6_population_female  literates_total  literates_male  \\\n",
       "state_name                                                               \n",
       "WEST BENGAL                    690382         14606480         7823632   \n",
       "UTTAR PRADESH                 1330427         17669408         9858998   \n",
       "MAHARASHTRA                   1853085         30514278        16794767   \n",
       "ANDHRA PRADESH                 863253         13463769         7178688   \n",
       "TAMIL NADU                     620051         11385700         5920803   \n",
       "\n",
       "                literates_female  sex_ratio  child_sex_ratio  \\\n",
       "state_name                                                     \n",
       "WEST BENGAL              6782848      57539            57499   \n",
       "UTTAR PRADESH            7810410      56151            55073   \n",
       "MAHARASHTRA             13719511      39582            38014   \n",
       "ANDHRA PRADESH           6285081      41975            39630   \n",
       "TAMIL NADU               5464897      32033            30822   \n",
       "\n",
       "                effective_literacy_rate_total  effective_literacy_rate_male  \\\n",
       "state_name                                                                    \n",
       "WEST BENGAL                           5361.31                       5570.79   \n",
       "UTTAR PRADESH                         4995.87                       5288.94   \n",
       "MAHARASHTRA                           3862.60                       4044.74   \n",
       "ANDHRA PRADESH                        3398.97                       3644.80   \n",
       "TAMIL NADU                            2882.98                       3003.35   \n",
       "\n",
       "                effective_literacy_rate_female  total_graduates  \\\n",
       "state_name                                                        \n",
       "WEST BENGAL                            5137.91          2668776   \n",
       "UTTAR PRADESH                          4667.93          3692166   \n",
       "MAHARASHTRA                            3665.93          5266151   \n",
       "ANDHRA PRADESH                         3154.26          2751351   \n",
       "TAMIL NADU                             2763.26          2144970   \n",
       "\n",
       "                male_graduates  female_graduates  \n",
       "state_name                                        \n",
       "WEST BENGAL            1561629           1107147  \n",
       "UTTAR PRADESH          2090734           1601432  \n",
       "MAHARASHTRA            2908234           2357917  \n",
       "ANDHRA PRADESH         1668337           1083014  \n",
       "TAMIL NADU             1182839            962131  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lis = dt.groupby('state_name').sum() # grouping to get the data by State-wise\n",
    "lis.sort_values(by = 'effective_literacy_rate_total', ascending=False, axis=0, inplace=True) #sorting to get the top values\n",
    "lis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_name\n",
       "WEST BENGAL                  18063509\n",
       "UTTAR PRADESH                25302925\n",
       "MAHARASHTRA                  37722136\n",
       "ANDHRA PRADESH               18171615\n",
       "TAMIL NADU                   13879395\n",
       "MADHYA PRADESH               11023091\n",
       "GUJARAT                      17835049\n",
       "RAJASTHAN                    10443016\n",
       "KARNATAKA                    15799896\n",
       "BIHAR                         6714516\n",
       "HARYANA                       5952223\n",
       "PUNJAB                        5821876\n",
       "NCT OF DELHI                 13481997\n",
       "ORISSA                        3003234\n",
       "JHARKHAND                     4278259\n",
       "CHHATTISGARH                  3137918\n",
       "KERALA                        2755973\n",
       "UTTARAKHAND                   1340397\n",
       "ASSAM                         1391154\n",
       "JAMMU & KASHMIR               1804987\n",
       "PUDUCHERRY                     541801\n",
       "MIZORAM                        291822\n",
       "HIMACHAL PRADESH               169758\n",
       "TRIPURA                        399688\n",
       "MEGHALAYA                      143007\n",
       "MANIPUR                        264986\n",
       "ANDAMAN & NICOBAR ISLANDS      100608\n",
       "NAGALAND                       123777\n",
       "CHANDIGARH                     960787\n",
       "Name: population_total, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lis['population_total'] = lis['population_total']/1000\n",
    "lis['population_total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15  4  2  2  4  0  1  0  0  1]\n",
      "[  100608.   3862760.8  7624913.6 11387066.4 15149219.2 18911372.\n",
      " 22673524.8 26435677.6 30197830.4 33959983.2 37722136. ]\n"
     ]
    }
   ],
   "source": [
    "# np.histogram returns 2 values\n",
    "count, bin_edges = np.histogram(lis['population_total'])\n",
    "\n",
    "print(count) # frequency count\n",
    "print(bin_edges) # bin ranges, default = 10 bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `histrogram` method breaks up the dataset into 10 bins. The figure below summarizes the bin ranges and the frequency distribution of population in states. We can see that:\n",
    "* 15 State contributed between 0 to 100608 people \n",
    "* 11 countries contributed between 100608. to 3862760.8 people\n",
    "* 1 country contributed between 3862760.8  to 7624913.6 people, and so on.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily graph this distribution by passing `kind=hist` to `plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "lis['population_total'].plot(kind='hist', figsize=(10, 7))\n",
    "\n",
    "plt.title('Histogram of Population in Different States from the sensus') # add a title to the histogram\n",
    "plt.ylabel('Number of States') # add y-label\n",
    "plt.xlabel('Number of people') # add x-label\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above plot, the x-axis represents the population range of people intervals of 10068. The y-axis represents the number of States that contributed to the aforementioned population. \n",
    "\n",
    "Notice that the x-axis labels do not match with the bin size. This can be fixed by passing in a `xticks` keyword that contains the list of the bin sizes, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "lis['population_total'].plot(kind='hist', figsize=(8, 5), xticks=bin_edges)\n",
    "\n",
    "plt.title('Histogram of Population in Different States from the sensus') # add a title to the histogram\n",
    "plt.ylabel('Number of States') # add y-label\n",
    "plt.xlabel('Number of people') # add x-label\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Charts (Dataframe) <a id=\"10\"></a>\n",
    "\n",
    "A bar plot is a way of representing data where the *length* of the bars represents the magnitude/size of the feature/variable. Bar graphs usually represent numerical and categorical variables grouped in intervals. \n",
    "\n",
    "To create a bar plot, we can pass one of two arguments via `kind` parameter in `plot()`:\n",
    "\n",
    "* `kind=bar` creates a *vertical* bar plot\n",
    "* `kind=barh` creates a *horizontal* bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vertical bar plot**\n",
    "\n",
    "In vertical bar graphs, the x-axis is used for labelling, and the length of bars on the y-axis corresponds to the magnitude of the variable being measured. Vertical bar graphs are particuarly useful in analyzing time series data. One disadvantage is that they lack space for text labelling at the foot of each bar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the number the literates of each States."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_name\n",
       "ANDAMAN & NICOBAR ISLANDS       81908\n",
       "ANDHRA PRADESH               13463769\n",
       "ASSAM                         1154871\n",
       "BIHAR                         4778390\n",
       "CHANDIGARH                     743254\n",
       "CHHATTISGARH                  2413475\n",
       "GUJARAT                      14154728\n",
       "HARYANA                       4484676\n",
       "HIMACHAL PRADESH               147799\n",
       "JAMMU & KASHMIR               1226215\n",
       "JHARKHAND                     3220027\n",
       "KARNATAKA                    12474974\n",
       "KERALA                        2401557\n",
       "MADHYA PRADESH                8391426\n",
       "MAHARASHTRA                  30514278\n",
       "MANIPUR                        219261\n",
       "MEGHALAYA                      119313\n",
       "MIZORAM                        253588\n",
       "NAGALAND                        95646\n",
       "NCT OF DELHI                 10379026\n",
       "ORISSA                        2458782\n",
       "PUDUCHERRY                     440125\n",
       "PUNJAB                        4431078\n",
       "RAJASTHAN                     7580529\n",
       "TAMIL NADU                   11385700\n",
       "TRIPURA                        343633\n",
       "UTTAR PRADESH                17669408\n",
       "UTTARAKHAND                   1019379\n",
       "WEST BENGAL                  14606480\n",
       "Name: literates_total, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lis = dt['literates_total']\n",
    "lis = lis.groupby('state_name').sum()\n",
    "lis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step 2: plot data\n",
    "lis.plot(kind='bar', figsize=(20, 10))\n",
    "\n",
    "plt.xlabel('States') # add to x-label to the plot\n",
    "plt.ylabel('Population') # add y-label to the plot\n",
    "plt.title('State-wise Literates') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>literates_total</th>\n",
       "      <th>literates_male</th>\n",
       "      <th>literates_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANDAMAN &amp; NICOBAR ISLANDS</th>\n",
       "      <td>81908</td>\n",
       "      <td>44908</td>\n",
       "      <td>37000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDHRA PRADESH</th>\n",
       "      <td>13463769</td>\n",
       "      <td>7178688</td>\n",
       "      <td>6285081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAM</th>\n",
       "      <td>1154871</td>\n",
       "      <td>613955</td>\n",
       "      <td>540916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIHAR</th>\n",
       "      <td>4778390</td>\n",
       "      <td>2680498</td>\n",
       "      <td>2097892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHANDIGARH</th>\n",
       "      <td>743254</td>\n",
       "      <td>426071</td>\n",
       "      <td>317183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           literates_total  literates_male  literates_female\n",
       "state_name                                                                  \n",
       "ANDAMAN & NICOBAR ISLANDS            81908           44908             37000\n",
       "ANDHRA PRADESH                    13463769         7178688           6285081\n",
       "ASSAM                              1154871          613955            540916\n",
       "BIHAR                              4778390         2680498           2097892\n",
       "CHANDIGARH                          743254          426071            317183"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lis = dt[['literates_total', 'literates_male', 'literates_female']]\n",
    "lis = lis.groupby('state_name').sum()\n",
    "lis = lis.head()\n",
    "lis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create the plot\n",
    "lfm = lis.plot(kind='bar', \n",
    "            figsize=(20, 8), \n",
    "            width=0.8, \n",
    "            color=['#5cb85c', '#5bc0de', '#d9534f'],\n",
    "           )\n",
    "\n",
    "lfm.set_title(\"State-wise Literates with male and female\", fontsize=16)\n",
    "lfm.spines['left'].set_visible(False)\n",
    "lfm.spines['top'].set_visible(False)\n",
    "lfm.spines['right'].set_visible(False)\n",
    "lfm.axes.get_yaxis().set_visible(False)\n",
    "lfm.tick_params(labelsize=14)\n",
    "lfm.legend(fontsize=14)\n",
    "for p in lfm.patches:\n",
    "    lfm.annotate(np.round(p.get_height(),decimals=2), \n",
    "                (p.get_x()+p.get_width()/2., p.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 14\n",
    "               )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}