Scatter_plots.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",
       "<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",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_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",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_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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       "<style scoped>\n",
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       "        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>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": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the data:\n",
    "\n",
    "Using a `scatter plot`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import `Matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.0.3\n"
     ]
    }
   ],
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lis = dt.groupby('state_name').sum() # grouping to get the data by State-wise\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>total_graduates</th>\n",
       "      <th>female_graduates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12420</td>\n",
       "      <td>5703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2751351</td>\n",
       "      <td>1083014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>242490</td>\n",
       "      <td>101658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>890106</td>\n",
       "      <td>287753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>189312</td>\n",
       "      <td>87821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_graduates  female_graduates\n",
       "0            12420              5703\n",
       "1          2751351           1083014\n",
       "2           242490            101658\n",
       "3           890106            287753\n",
       "4           189312             87821"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fgd = lis.loc[:,['total_graduates', 'female_graduates']]\n",
    "fgd.set_index('total_graduates',inplace=True)\n",
    "fgd.reset_index(inplace=True)\n",
    "fgd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fgd.plot(kind='scatter', x='total_graduates', y='female_graduates', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Number of female Graduates from total graduates')\n",
    "plt.xlabel('Total Graduates')\n",
    "plt.ylabel('Female Graduates')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data. We can mathematically analyze this upward trend using a regression line (line of best fit). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "- `x`: x-coordinates of the data. \n",
    "- `y`: y-coordinates of the data. \n",
    "- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.34948637e-01, -5.23310725e+03])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = fgd['total_graduates']      # year on x-axis\n",
    "y = fgd['female_graduates']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[4.34948637e-01, -5.23310725e+03]` with the the slope in position 0 and intercept in position 1. \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q6lDvdBhUDIqIiIi0kEmThjF0aDEDBnRh6NBiJk0aFnZKGkAiIiIi0lLKyrowdeqosNOIo5ZBERERkTymYlBEREQkj6kYFBEREcljKgZFRERE8piKQREREZEWZFavhs2bw04jSqOJRURERFpAwYoV9BkyBIDqHXZg+ZtvhpyRTy2DIiIiIplUXU2Pn/wkWggCVE2ZEmJC8VQMioiIiGRIlz//mZIdd6T9nDkArL76aioiEap33TXkzL6n08QiIiIizaz9q6/S4/TTo9MbjzySqgcfhDZtQswqNRWDIiIiIs2kTSRC7wMOiE577dqxdO5cvKKiELOqn4pBERERkW21aRM9jz+etp9+Gg2teP55tuyzT4hJpUd9BkVERES2wfbXXkvJTjtFC8Fvb7mFikikVRSCoJZBERERkSbp8PzzFJ13XnT6u5NP5ts77wRjQsyq8VQMioiIiDRCm/nz6X344dHpml69WP7aa3hduoSYVdOpGBQRERFJg1m/nl6HH06bJUuiseWzZlE9aFCIWW079RkUERERqY/n0e1Xv6LvLrtEC8Gqv/3Nv15gKy8EQcWgiIiISJ06/vOflPTrRyfnAFh37rlURCJs/PGPQ86s+eg0sYiIiEiCwo8+otfRR0ent+y6Kyuefx46dAgxq8xQMSgiIiJSa9Uq+gwcSMGGDdHQsjlzqCkrCzGpzNJpYhEREZGtW+l+9tm069MnWgiufOQRKiKRnC4EQcWgiIiI5LnO999PyQ470HH6dADW/upXVEQibBo5MuTMWoZOE4uIiEheavf22xSfdFJ0etMBB2BmzmTt6tUhZtXyVAyKiIhIXilYvpw+++4bF1s6bx5be/emuG3bkLIKj4pBERERyQ/V1fSwlvZvvRUNVT75JJsPOCDEpMKnPoMiIiKS87rcfjslO+4YLQRXX3MNFZFI3heCoJZBERERyWHtZ82ixxlnRKc3jBrFqgcegAK1h9VSMSgiIiI5p015Ob0PPDA6vbVjR5a98w5e9+4hZpWdVAyKiIhI7ti4kZ7HH0/bzz6LhpZPm0b1XnuFmFR2UxupiIiI5ITtf/97SnbeOVoIrrrtNioiERWCDVDLoIiIiLRqHZ59lqJf/CI6/Z21fHvbbWBMiFm1HioGRUREpFUqnD+fXocfHp2u6duX5a+8gte5c4hZtT4qBkVERKRVMevW0evww2mzdGk0tvyVV6geODDErFov9RkUERGR1sHz6HbxxfTddddoIVh1771+v0AVgk2mYlBERESyXsfHH6ekXz86/etfAKw77zwqIhE2Hn98yJm1fjpNLCIiIlmr7Ycf0vOYY6LTW3bfnRXPPgsdOoSYVW5RMSgiIiJZx6xaRZ/998ds3BiNLXvrLWr69Qsxq9yk08QiIiKSPbZupWjsWPrutVe0EFz56KNURCIqBDNExaCIiIhkhc733kvJDjvQYcYMANb8+tdURCJsirl8jDS/FjlNbK3dAXgY6ANsBe51zt1hrS0C/gn0BxYC1jm3ylprgDuA44DvgHHOuXnBusYCvwtWPdE5NyWIDwUmAx2B54GLnXNeU7YhIiIiLafdW29RfPLJ0elNBx3Eyscfh7ZtQ8wqf7RUy2A1cKlzbnfgIOAia+0ewBXAS865QcBLwTTAscCg4HE+cDdAUNj9HjgQOAD4vbW29o7Tdwfz1i5X29u0UdsQERGRllGwbBklpaVxheDS995j5f/9nwrBFtQixaBzbkltq5tzbi3wKVAKjAamBLNNAU4Mno8GHnbOec65N4Fu1tq+wNHAi865KufcKuBF4Jjgte2dc3Occx5+K2TsuhqzDREREcmkLVsoHj2aPvvtFw1VPvUUFZEIW3v1CjGx/NTio4mttf2BfYG3gN7OuSXgF4zW2tp3QCmwOGax8iBWX7w8RZwmbGNJQr7n47cc4pyjuLi4kb9x+goLCzO6fmmYjkG4tP/Dp2MQvlw/Bm2uv542EydGp6v/9Ce2jh/P9iHmFCvX938qLVoMWmu7AP8HXOKcW2OtrWvWVHeW9poQr09ayzjn7gXurX29srKygdU2XXFxMZlcvzRMxyBc2v/h0zEIX64eg/Yvv0yPM8+MTm845hhW3XcfFBRAFv2+ubT/S0pK0pqvxUYTW2vb4heC/3DO/TsIL6s9NRv8XB7Ey4EdYhbvB1Q0EO+XIt6UbYiIiEgzabN4MSWlpdFCcGvnziz5+GNWPfCAXwhK6FrkKAQjdx8APnXO3Rbz0lRgbPB8LPB0TPwsa62x1h4ErA5O9U4DRllruwcDR0YB04LX1lprDwq2dVbCuhqzDREREdlWGzfS84gj6H3QQdHQ8mnTWPrFF3jduoWYmCRqqdPEhwBjgA+ttf8NYlcCNwLOWnsu8A1wavDa8/iXfJmPf9mXswGcc1XW2uuBd4L5rnPOVQXPL+D7S8u8EDxo7DZERERk22x/9dV0eeCB6PSq229nQ91dwyRkxvMa6lonMbyKisydSc6lfgqtlY5BuLT/w6djEL7WfAw6TJ1K0QUXRKfXn3Yaq2+9FUyqbvrZqTXv/0RBn8EGd77uTSwiIiLbpPCLL+h1xBHR6erSUla8/DJe584hZiXpUjEoIiIiTWLWrqXX8OG0iWlJW/bKK9QMHBhiVtJYGsYjIiIijeN5dJswgb677RYtBKvuv5+KSESFYCukYlBERETS1unRRynp149O//avErfuF7+gIhJh47HHhpyZNJVOE4uIiEiD2n7wAT1jCr4te+7JimeegfbtQ8xKmoOKQREREamTqaqiz9ChmM2bo7Flb79NTWlpPUtJa6LTxCIiIpKspoaiMWPoO3hwtBBc+dhjfr9AFYI5RcWgiIiIxOn8t79RUlZGh5kzAVhz+eVURCJsOuywkDOTTNBpYhEREQGg3ZtvUnzKKdHpTcOGsfKxx6BQ5UIu09EVERHJcwVLl9Jn6NC42NL//petPXuGlJG0JBWDIiIi+WrLFopPPpl28+ZFQyueeootP/hBiElJS1OfQRERkTy03c03U9K/f7QQ/HbiRCoiERWCeUgtgyIiInmk/YwZ9Bg7Njq94bjjWHXPPVCg9qF8pWJQREQkD7RZtIjew4ZFp7dutx3L3noLr2vXELOSbKBiUEREJJdt2ECvUaMo/PrraGj59OlU77lniElJNlGbsIiISC7yPLpeeSUlAwdGC8FVd9xBRSSiQlDiqGVQREQkx3R4+mmKLrwwOr3+Zz9j9c03gzEhZiXZSsWgiIhIjij8/HN6jRwZna7eYQdWzJyJ16lTiFlJtlMxKCIi0sqZtWvpNWwYbaqqorFlr71GzU47hZiVtBbqMygiItJaeR7dLryQvrvtFi0Eqx54gIpIRIWgpE3FoIiISCvU6e9/p6RfPzo9/TQA6y64gIpIhI3HHBNyZtLa6DSxiIhIK9L2v/+l5/HHR6c3Dx5M5dNPQ/v2IWYlrZmKQRERkVagoKqK3kOGYGpqorGlb7/N1tLSELOSXKDTxCIiItmspoaiM86gz+DB0UKw8vHHqYhEVAhKs1AxKCIikqU63303JWVldJg1C4A1V1xBRSTC5kMPDTcxySk6TSwiIpJl2s2eTfGpp0anNx16KCv//nco1J9taX56V4mIiGSJgiVLaFdaSnFMbOn777O1uLjOZUS2lYpBERGRsG3eTPHJJ9PuvfeioRXPPMOW/fYLMSnJF+ozKCIiEqLtbriBkgEDooVg9R13UBGJqBCUFqOWQRERkRC0f/FFeowbF53ecPzxrPrb3yju1QsqK8NLTPKOikEREZEW1GbhQnofckh0emvXriybMweva9cQs5J8pmJQRESkBZgNG+h55JEULlwYjS2fMYPq3XcPLykR1GdQREQkszyPrldcQd+BA6OF4Kq//IWKSESFoGQFtQyKiIhkSMcnn6T7+PHR6fVjxrD6hhvAmBCzEomnYlBERKSZFX72Gb1++MPodHX//qyYMQOvY8cQsxJJTcWgiIhIMzFr1tD74IMp+PbbaGzZ669TM2BAiFmJ1E99BkVERLaV59H9l7+k7+67RwvBlQ89REUkokJQsp6KQRERkW3Q6eGHKenXj47PPAPA2vHjqYhE2DRqVMiZiaRHp4lFRESaoO28efT88Y+j05v32YfKp56Cdu1CzEqk8VQMioiINELBypX02XvvuNjSd95ha0lJSBmJbBudJhYREUlHTQ1Fp58eVwhW/vOfVEQiKgSlVVMxKCIi0oAud91FSVkZHV59FYA1V15JRSTC5uHDQ85MZNvpNLGIiEgd2r3xBsXWRqc3Hn44VY88Am3ahJiVSPNSMSgiIhJj0aK1XP+L53j+w0ujMa+ggGX//S9be/QIMTORzGjSaWJr7RHW2sOaOxkREZEwmfXrOXjYbnGF4NjdrmLJ4sUqBCVnpVUMWmtfsdYeEjz/DfA48Ji19spMJiciItJSSkpL6bvLLtFpx94YbuG1TRocIrkt3dPEewFvBs/PA0YA64A3gD82f1oiIiIto+tll9H50UfjYgXchBe0lxQVdQgjLZEWk24xWAB41tqdAeOc+xTAWts9Y5mJiIhkUPuXX6bHmWfGxZa+/TYLa7qy3/jZVFVtpKioA5MmDQspQ5GWkW4x+DowCegLPAkQFIaVGcpLREQkIwqqqugzeHBcrOqvf2Xj6NEAlAFTp+pWcpI/0i0GxwGXAiuAW4LYbsAdGchJRESk+XkeJf36xYU2HnUUVZMnh5OPSJZIqxh0zq0ErkyIPZeRjERaqW+++YYLL7yQVatWMXjwYO68807atfA9ShcvXsyIESPYaaedANhvv/246aab2LBhA+effz6LFi2iTZs2HHXUUVx5pf+Rfvjhh5kyZQoFBQV07tyZm2++mV122YX33nuPyy+/HADP87j00ks59thjW/T3EWkuRWPG0GHmzLhYRXk5GBNSRiLZw3ie1+BM1tr2wNXA6UAP51xXa+0oYBfn3KQM55hNvIqKioytvLi4mMpKnXkP07Ycg1/84hccd9xxjB49mt/85jfssccejB07dpvyueSSS7DWMmxYen2WFi9ezNixY5mZ8Edvw4YNzJs3j0MOOYTNmzfz05/+lAkTJjBy5EjWrl3LdtttB8D06dOZMmUK//jHP9iwYQNt27alsLCQZcuWcdRRRzFv3jwKCzN3eVJ9BsKXa8eg45NP0n38+LjYkg8/xCsqCimjhuXaMWhtcmn/l/i3SWzwP550rzN4O/6I4jOA2urxY+CCpiQnkq2uueYa7r///uj0jTfeyAMPPNDgcp7n8cYbb3D88ccDcOqppzJt2rSk+a666ipuv/12AGbNmsXJJ5/M1q1bmyn7unXs2JFDDjkEgHbt2jF48GCWLFkCEC0EAb777jtM0FLSsWPHaOG3adOmaFykNWhTXk5JaWlcIbjysceoiESyuhAUCUO6/+KfBAx0zq231m4FcM5FrLWlmUtNpOWNGzeOU045hZ///Ods3bqVqVOn8uyzz7Ju3TpOOumklMvcddddFBcX07Vr12jx1LdvX5YuXZo075VXXslxxx3HAQccwFVXXcUjjzxCQUHz3iL8m2++YdSoUWy33XZcfvnlHHjggXGvr169mhdffJFzzz03Gps8eTL33nsvmzdvxjkXjc+bN49LL72U8vJy7rzzzoy2Coo0i5oaSsrK4kLrx4xh9Y03hpSQSPZL95t9c+K81tqewMp0FrbWPgj8CFjunNsriF2Df83CFcFsVzrnng9e+y1wLlAD/D/n3LQgfgz+oJU2wP3OuRuD+AD8C2EXAfOAMc65zcHp7YeBoUGuP3XOLaxvG5Lf+vfvT/fu3fnoo49YsWIFe+65J0VBK8KLL75Y53IrVyZ/FFK1pHXs2JGbb76ZU045hWuuuYb+/fsnzTNr1iz+8Ic/AFBRUcE777xDp06daN++Pc8++2y9+ffq1Yu3336boqIiPvjgA8455xxefvnlaOtfdXU1F110Eeeccw477rhjdLlx48Yxbtw4nnzySe644w7uuMMfG7bffvvx8ssv8+WXX3LJJZdwxBFH0KGDrrkm2annyJG0/fzzuFhFJBJSNiKtR7rF4BPAFGvtrwCstX2BP+MXYOmYjH9pmocT4rc7526NDVhr9wBOA/YESoCcOXNxAAAgAElEQVQZ1traS8LfBRwFlAPvWGunOuc+AW4K1vW4tfZv+EXe3cHPVc65gdba04L5flrXNpxzNWn+PpLDTj/9dJxzLF++nNNOOw2gwZbBQYMGsXr1aqqrqyksLGTJkiX07t075fyfffYZ3bt3Z9myZSlfHzFiBCNGjAAa7jP4wgsvcNtttwFw6623ss8++9C+fXsA9t57b/r378/XX3/NPvvsA8Dll1/OgAEDOO+881Kub/To0fz2t79Nig8aNIiOHTvy+eefR9clki0633cfXa+5Ji625Msv8Tp1CichkVYm3WLwSuBm4EOgE/AlcB9wXToLO+detdb2T3Nbo4HHnXObgAXW2vnAAcFr851zXwNYax8HRltrPwVGAj8L5pkCXINfDI4OngP8C5hkrTX1bGNOmjlKDjv22GO59dZbqa6u5q677gKgS5cu9bYMAgwbNoznnnuO0aNH88QTTzBqVPJ1ysrLy7nnnnuYNm0aY8aM4eijj2a//fbbplxjR/iuXLmSbt260aZNGxYtWsSCBQsoC06Z3XTTTaxdu5Zbb437/4uvv/46Ovp4xowZDBgwAPBPN5eUlFBYWEh5eTlff/01O+ywQ5NzFWluhZ9/Tq+RI+NiK557ji1DhoSUkUjrlO6lZTYDlwCXBKeHK51zDQ9Dbth4a+1ZwLvApc65VUAp39/6DvxWwNq+iYsT4gcCPYBvnXPVKeYvrV3GOVdtrV0dzF/fNuJYa88Hzg/WQXFxcRN+zfQUFhZmdP3SsMLCQkpKShg5ciTdunWrs3UvlVtvvZUxY8Zw6623MmTIECZMmBBtpQN/kMmZZ57JLbfcwl577cUDDzzAz3/+c2bPnl3nqdcOHTrQtWvXtN8Xr732Gtdeey2FhYW0adMm2mpZ2+dv1113jQ5yueCCCzjnnHP44x//yMyZM2nbti3du3dnypQpFBcXM23aNM455xzatm1LQUEBf/nLX9gl5r6tmaDPQPhaxTHYtIl2228fF6q++mq2/u//0jWklJpTqzgGOSwf939axaC1tso5VwTgnFsRE1/unOvVxG3fDVyPPzr5euBPwDmkHgLtkXrks1fP/NTzWn3LxHHO3QvcWztPJoeb59Jw9taquLiY5cuXM3v2bO65555GHY/tt9+ep59+Ojq9du1a1q5dGzfP3//+dwAqKyspKytj+vTprFu3jnXr1qVc541Bp/d08zj00EOZMWNGXKyyspIOHToQSdF3qrKykiuvvDJ6zcHY+NFHH83RRx+dFM8kfQbCl+3HoM8uu1Cwfn10uqa4mGXvv+9PZHHejZHtxyDX5dL+Dy4t06B0hzG2TQxYa9viD+RoEufcMudcjXNuK/4p59pTweVA7LmofkBFPfFKoJu1tjAhHreu4PWuQFU965I89+mnn3LIIYcwfPjw6KlTEQnfdjfcQElpaVwhWLFw4feFoIg0Wb0tg9ba1/BbzDpYa19NeLkfMLupG7bW9nXOLQkmTwI+Cp5PBR611t6GP7hjEPA2fmveoGDkcAR/AMjPnHOetfZl4Cf4A1rGAk/HrGssfl/AnwAzg/nr2obkud133505c9R1VCRbtH33XXoG9wyuteyVV6gZODCkjERyT0Onie/HL8J+AMReedcDlgEzUy2UyFr7GDACKLbWlgO/B0ZYa4cE61oI/ALAOfextdYBnwDVwEW1o3ytteOBafgtkg865z4ONvEb4HFr7UTgvZhcHwAeCQaIVOEXkPVuQ0REwmfWraPvrrvGxb694Qa+O+uskDISyV3p3o5uN+fcZy2QT7bT7ehynI5BuLT/w5cNx6CkNH483+YhQ6h87rmQsml52XAM8lku7f90b0eX7mjiz6y1vfH79RXHrtg592ATcxQREYnqdskldHriibhYxeLF0Mx36RGReOmOJj4R+Dv+9QX3xL8v8V7A64CKQRERabL2M2bQY+zYuNjSd99la9++IWUkkl/S/XdrInC2c25fYH3w83xgbsYyExGRnFZQWUlJaWlcIVj1t79REYmoEBRpQenegaTMOfdEQmwKsBT4dfOmJCIiOc3zKOnXLy604dhjWXX//SElJJLf0m0ZXB70GQRYaK09GNiZbbjOoIiI5J8ep52WVAhWlJerEBQJUbrF4H3A8OD57cDLwPvAXzORlIiI5JaO//oXJaWltH/ttWhsyUcfURGJgGlwsKOIZFC6o4lvinn+sLV2FtDZOfdpphITEZHWr83ixfQ+6KC4WOXjj7P50ENDykhEEqXbZzCOc+6b5k5ERERySE0NJWVlcaF1Z5/NmokTQ0pIROqS7qVlFuPfKSSJc64sVVxERPJTz8MPp+38+XGxikgkpGxEpCHptgyemTDdF7gY/17AIiIidL7nHrped11cbMn8+XgdO4aUkYikI90+g68kxoJ+g/8B7mjmnEREpBUp/PRTeh15ZFxsxQsvsGXvvUPKSEQao0l9BgObgAHNlYiIiLQyGzdSsvPOcaE1l13GuksuCSkhEWmKdPsMXpcQ6gQcB7zQ7BmJiEjW67vzzpiNG6PTNX36sGyubkol0hql2zK4Q8L0euA24JHmTUdERLLZ9hMn0uXuu+NiFQsXQtu24SQkItss3T6DZ2c6ERERyV7t3n6b4pNOioste+01anbaKaSMRKS51FkMWmtHprMC59zM5ktHRESyiVm7lr677RYX+/amm/juzMSLTIhIa1Vfy+ADCdOl+NcaXAn0AAxQDujfQhGRHFRSWho3vXnoUCqnTg0pGxHJlDqLQedcdKSwtfZK/ALwKufcd9baTsB1+IWhiIjkkG4TJtDp3/+Oi1UsXgwF6d7OXkRak3Q/2b8CrnDOfQcQ/Pwt8D+ZSkxERFpW++nTade+fVwhuHTuXP/uISoERXJWuqOJ1wMHAG/ExH4AfNfsGYmISIsqWLGCPkOGxMWq7ruPjccdF1JGItKS0i0GrwL+Y619BliMf6mZHwEXZSoxERHJMM+jpF+/uFDNKaew7M47Q0pIRMKQVru/c+4R4EDgU2B74DPgoCAuIiKtTI+f/CSpEKyIRKh59NGQMhKRsKR9Ozrn3CfAJxnMRUREMqzjP/9J9/+J7+695JNP8Lp2DSkjEQlb2sWgtfYE4HCgGP+yMgA4587KQF4iItKM2ixaRO9hw+JilU88weaEmIjkn7ROE1trfw/cE8x/Kv4lZY4Gvs1caiIiss2qqykpLY0rBNedey4VkYgKQREB0r+0zDnAUc65XwGbg58/BvpnKjEREdk2vQ45hJIdd4yLVUQirLnuupAyEpFslG4x2M0591HwfLO1tq1z7m3808YiIpJFOt99NyWlpRQuXBiNVcyf718vUEQkQbrF4FfW2j2D5x8BF1hrxwCrMpOWiIg0VuHHH1NSWkrXiROjseXTpvlFYMeOIWYmItks3QEkv8O/HR3AFcCjQBfgwkwkJSIijbBxIyU77xwXWvPb37Ju/PiQEhKR1qTBYtBaWwBsBN4ECE4PD8xwXiIikoa+ZWWYmprodHW/fix/660QMxKR1qbB08TOua3A0865zS2Qj4iIpGH7a6+lpLQ0rhCsWLRIhaCINFq6fQZftdYelNFMRESkQe3efJOS0lK63HtvNLbsjTf8foGFaV86VkQkKt1vjkXAC9bap/HvTezVvuCcuzoTiYmIyPcKVqygz5AhcbFVf/oTG047LaSMRCRXpFsMdgSeCp73q29GERFpXiWlpXHTmw48kJX//ndI2YhIrkmrGHTOnZ3pREREJF7fAQMwm+O7a1csXgwF6fbwERFpWDqjids657YEz4cT389wtnOuOlPJiYjkoy533cX2f/xjXGzZ669TM2BASBmJSC6rtxi01l4ADAPGBKHp+PclBugEXA48kLHsRETySOEXX9DriCPiYmuuvJJ1F10UUkYikg8aahk8C/hlzPQm59wOANbaIcDdqBgUEdk2W7dSssMOcSGvXTuWLFgQUkIikk8aKgYHOOfej5n+JOb5+8BOzZ+SiEj+SBwcAugewiLSohrqhdzFWtu5dsI5d0jMa52AzsmLiIhIQ7qfd15SIbh07lwVgiLS4hoqBj8CRtXx2jHAx82bjohIbqu9aHTH55+Pxr6dOJGKSIStffqEmJmI5KuGThP/GfirtdYDpjrntgb3Kh4NTAL+J9MJiojkhE2bKNkpvmfN1g4dWPrVVyElJCLiq7cYdM49bq0tBf4OtLPWVgLFwCbgOufcYy2Qo4hIq5ZOv8BFi9YyYcIcqqo2UlTUgUmThlFW1qWlUhSRPNbglUudc38CSoAfA5cBJwD9nHO3ZDg3EZGss2jRWk44YTrDh0/lhBOm88036+qct+cPf5hUCC759NOU/QInTJjD3LmVLFiwjrlzKxk/fnaz5y4ikkq6dyBZA0zLcC4iIlmvtmgDWLBgHePHz2bq1Piu1R2eeYaiX/4yLlZ1331sPO64OtdbVbWx3mkRkUxJ997EIiJC/UWbWb2avnvsEff65sGDqfzPfxpcb1FRBxYsWBc3HUunkUUkU1QMiog0Ql1F27ZeL3DSpGGMHz87rtiLlU6LpIhIU6gYFBFphMSi7d25Z0NCHVixYAG0a9eo9ZaVdam3uNNpZBHJlAYHkNSy1vaw1o6x1l4eTJdYa/tlLjURkexTW7R98MO5fiEY4+e9L2T/oQ/xzdLNzb7dxNPGidMiIk2VVjForT0c+Bw4A7gqCA/CvzexiEjeKPzsM0pKS+ly//3R2ML2fTDcwgPLBmRsJPCkScMYOrSYAQO6MHRocdJpZBGRpkr3NPGfgZ86516y1q4KYm8BB2QmLRGR7FNXv8CRw6dCTD/CTJzCbeg0sohIU6VbDPZ3zr0UPPeCn5sbsbyISKuVsghcvBgK/JMrDY0EFhHJZun2GfzEWnt0QuxI4MNmzkdEJGv0OuCApEKw8l//8kcJF3z/9alTuCLSmqXbsncp8Ky19jmgo7X2Hvw7kozOWGYiIiFp/8or9PjZz+JiW3bdlRUzZ6acX6dwRaQ1S/cOJG9aa/fBH0DyILAYOMA5V57O8tbaB4EfAcudc3sFsSLgn0B/YCFgnXOrrLUGuAM4DvgOGOecmxcsMxb4XbDaic65KUF8KDAZ6Ag8D1zsnPOasg0RyWM1NZSUlSWFG3O9QBGR1ibtS8s45yLOuZudcxc5525MtxAMTAaOSYhdAbzknBsEvBRMAxyLP1J5EHA+wYjloLD7PXAg/sCV31truwfL3B3MW7vcMU3Zhojkr5LS0qRCsCISUSEoIjmvzpZBa+0jfD9YpE7OubPSmOdVa23/hPBoYETwfAowC/hNEH/YOecBb1pru1lr+wbzvuicqwryexE4xlo7C9jeOTcniD8MnAi80NhtOOeWNPS7iEhuSTU4ZNmrr1Kz884hZCMi0vLqO008P8Pb7l1bfDnnllhrewXxUvzT0LXKg1h98fIU8aZsI6kYtNaej996iHOO4uLiRv6a6SssLMzo+qVhOgbhasn9X3D//RRedFFcrOb006mZPJnudSyTD/QZCJ+OQbjycf/XWQw6565tyURimBQxrwnxpmwjiXPuXuDe2nkqKysbWHXTFRcXk8n1S8N0DMLVEvvfrF9P3112SYpHTwfn+fHXZyB8OgbhyqX9X1JSktZ8aV8n0FrbDtgVKCammHLOpR5e17Bltadmg9PAy4N4ObBDzHz9gIogPiIhPiuI90sxf1O2ISI5rK6LRouI5LO0ikFr7XDgCaA9sD2wBtgO/1TrTk3c9lRgLHBj8PPpmPh4a+3j+INFVgfF3DTgjzGDRkYBv3XOVVlr11prD8K/K8pZwF+aso0m/h4iEpJFi9YyYcIcqqo2UlTUgUmThlFW1iVpvlRF4NIPP2RrUVFLpCkiktXSHU18O3Czc64IWBv8vB74azoLW2sfA+YAu1pry6215+IXaEdZa78Ejgqmwb80zNf4fRbvAy4ECAaOXA+8Ezyuqx1MAlwA3B8s8xX+4BEauw0RaV0mTJjD3LmVLFiwLuU9gbe/7rqkQnDN5ZdTEYmoEBQRCaR7mngX/OvyxboRWADc2tDCzrnT63jphynm9YCLUsyLc+5B/OscJsbfBfZKEV/Z2G2ISOuReA/g2umCJUvos//+SfPrlLCISLJ0i8HV+KeHvwWWWGv3AFYCyedjRERaSKp7AqtfoIhI46R7mvjf+HfrAHgAeBmYi9+PUEQkFLH3BPa4jHfnnh33esVXX6kQFBFpQLq3o7sk5vmfrLVv47cKTstUYiIiDSkr68KcbyfSdsFXcfFVd93FhhNPDCkrEZHWJe1Ly8Ryzr3W3ImIiDRG27lz6XnCCUlxtQSKiDROupeWKcO/L/C+JPQTdM4lX71VRCRTPI+Sfv2SwioCRUSaJt2WwSeAz4CrgQ2ZS0dEpG4pB4csXgwF6XZ/FhGRROkWg7sBBzvntmYyGRGRVFIVgSsffZRNhx8eQjYiIrkl3X+nnwH0rSsiLarD008nFYJbt9uOikhEhaCISDNJt2Xw/wGzrbVfActiX3DOndPsWYlIftuyhZL+/ZPC6hcoItL80i0GHwJqgE9Rn0ERaWa19xhevXoLX84/P+l1FYEiIpmTbjE4Eihxzq3NZDIikp8mTJiTdMFogOUvv0z1LrpggYhIJqVbDH4A9ABUDIpIs9r+6qt5d+4DcbHXO+zGTl+9FFJGIiL5Jd1icCYw3Vr7EMl9Bh9s9qxEJOeZqir6Dh6cHOcWhu5ZzNQQchIRyUfpFoPDgQgwKiHuASoGRaRRUl0qZv+hD7F69RaGdm3LpEnDQshKRCQ/pXtv4iMynYiI5L5UReDSefPY2rs3U4Hi4mIqKytbPjERkTyW9r2JrbU9gOOAPs65W6y1JUCBc648Y9mJSE4oHj2adu++Gxf77pRT+PbOO0PKSEREaqV7b+LDgf8D3gUOAW4BBgG/Bn6csexEpFUrnD+fXikuDq1LxYiIZI9070DyZ+CnzrljgOog9hZwQEayEpFWr6S0NKkQrIhEVAiKiGSZdE8T93fO1V7nwQt+bm7E8iKSJ1L1C6yYPx86dgwhGxERaUi6LYOfWGuPTogdCXzYzPmISCtVUlqaVAiuvuoqvyVQhaCISNZKt2XvUuBZa+1zQEdr7T34fQVHZywzEWkV2r/6Kj1OPz0prtPBIiKtQ1otg865N4G9gY/xryu4ADjAOfdOBnMTkWzmeZSUliYVguoXKCLSutTbMmit7eOcWwrgnKsAbm6RrEQkq6XsF1heDsaEkI2IiGyLhloGv4idsNb+O4O5iEiWS9UvcOWUKX5LoApBEZFWqaFiMPHbfUSG8hCRLNZp8uTUrYGRCJuOPDKEjEREpLk0NIDEa+B1EcllmzZRstNOSWH1CRQRyR0NFYOF1toj+L6FMHEa59zMTCUnIuGpqyVQRERyS0PF4HL80cO1ViZMe0Bys4GItFqpisDlM2ZQvfvuIWQjIiKZVm8x6Jzr30J5iEjIuv72t3R++OG42JY992TF9OkhZSQiIi1Bt5MTyXMFK1fSZ++9k+I6JSwikh9UDIrkMfULFBERFYMieShVEbj0/ffZWlwcQjYiIhKmtG5HJyK5ofjYY5MKwfVnnEFFJKJCUEQkT6llUCQPFH7+Ob1GjkyK65SwiIioGBTJcalOCRtuYejQYqaGkI+IiGQXFYMiOSpVEdieG9gcfOyrqja2dEoiIpKFVAyK5JhUReC3f/gDR/67hM1zK6OxoqIOLZmWiIhkKRWDIjmi/cyZ9BgzJile2y9w0sh1jB8/m6qqjRQVdWDSpGEtnaKIiGQhFYMirZ3nUdKvX1I4cXBIWVkXpk4d1VJZiYhIK6FiUKQVS3nR6PJyMCaEbEREpDVSMSjSCqUqAlc++iibDj88hGxERKQ100WnRVqRzvfdV+ct5FQIiohIU6hlUKQ12LiRkp13TgrrotEiIrKtVAyKZLm6WgJFRESag4pBkSyVqghcPmsW1YMGhZCNiIjkKvUZFMkyXS+9NKkQ3LzvvlREIlQPGsSiRWs54YTpDB8+lRNOmM4336wLKVMREckFahkUyRIFK1bQZ8iQpHjiKeEJE+YwN7iTyIIF/oWkdf1AERFpKhWDIlmgMf0CE+8prHsMi4jItlAxKBKiVEXgkg8/xCsqqnOZoqIOLFiwLm5aRESkqdRnUCQEPUeOTCoE1519NhWRSL2FIMCkScMYOrSYAQO6MHRose4xLCIi20QtgyItqPCjj+h19NFJ8cZcKkb3GBYRkeakYlCkheh6gSIiko1UDIpkWMoicMECaNcuhGxERETiqc+gSIaUlJYmFYLf3nST3xqoQlBERLJE6C2D1tqFwFqgBqh2zu1vrS0C/gn0BxYC1jm3ylprgDuA44DvgHHOuXnBesYCvwtWO9E5NyWIDwUmAx2B54GLnXNeXdvI8K8reaDDtGkUnXNOUlynhEVEJBtlS8vgEc65Ic65/YPpK4CXnHODgJeCaYBjgUHB43zgboCgsPs9cCBwAPB7a233YJm7g3lrlzumgW2INM3WrZSUliYVghWRiApBERHJWtlSDCYaDUwJnk8BToyJP+yc85xzbwLdrLV9gaOBF51zVUHr3ovAMcFr2zvn5jjnPODhhHWl2oZIo5WUllKyww5xsYrychWBIiKS9UI/TQx4wHRrrQfc45y7F+jtnFsC4JxbYq3tFcxbCiyOWbY8iNUXL08Rp55txLHWno/fsohzjuLi4ib/og0pLCzM6PqlYYnHYMGCbxk3bjqVlRsoLu7I5MlHM2BA1+jr7dq3T1rHlmnT8EaMQEey8fQZCJ+OQfh0DMKVj/s/G4rBQ5xzFUEx9qK19rN65jUpYl4T4mkLitN7a5etrKxszOKNUlxcTCbXLw1LPAZnnDE9eh/g+fNXc8YZzzF16ii6/PWvbP+HP8Qt67Vty5KFC/0JHccm0WcgfDoG4dMxCFcu7f+SkpK05gv9NLFzriL4uRx4Er/P37LgFC/Bz+XB7OVA7Lm4fkBFA/F+KeLUsw2RqMT7/n5XuYaS0tKkQrAiEvm+EBQREWlFQi0GrbWdrbXb1T4HRgEfAVOBscFsY4Gng+dTgbOstcZaexCwOjjVOw0YZa3tHgwcGQVMC15ba609KBiJfFbCulJtQyQq9r6/HpfxyaKL417X4BAREWntwm4Z7A28bq19H3gbeM459x/gRuAoa+2XwFHBNPiXhvkamA/cB1wI4JyrAq4H3gke1wUxgAuA+4NlvgJeCOJ1bUMkatKkYXhchsdlcfFlr72mIlBERHKC8bxGdaHLd15FRUXDczVRLvVTaK1ij0G3iy6i01NPxb2+6eCDWfmvf4WRWl7QZyB8Ogbh0zEIVy7t/6DPYKrxE3GyYQCJSFYpWLKEPvvvnxRXS6CIiOQiFYMiMdq1b0+fhJiKQBERyWUqBiUnLFq0lgkT5lBVtZGiog5MmjSMsrIuaS+feA9hgCUff4zXrVtzpikiIpJ1wh5AItIsJkyYw9y5lSxYsI65cysZP352Wsv1OvjgpEJw3fnnUxGJqBAUEZG8oJZByQmJ1wNMnE7U9oMP6HnssUnxzZs2sSZHOg6LiIikQ8Wg5ISiog4sWLAubrouqU4J1/YLzK8bEImIiKgYlBwxadIwxo+fHddnMFHKInDhQmjbtgUyFBERyU4qBiUnlJV1YerUUSlfS1UErrrtNjb89KeZTktERCTrqRiUnNXRObr/6ldJcV0qRkRE5HsqBiX31NRQUlaWFFYRKCIikkzFoOSU+gaHiIiISDIVg5ITUhWBKx97jE2HHRZCNiIiIq2HLjotrdr2115bZ2ugCkEREZGGqWVQWiWzbh19d901Ka5TwiIiIo2jYlBaHfULFBERaT4qBqXVSFUELnvlFWoGDgwhGxERkdygPoOS9XqceGJSIbhljz2oiERUCIqIiGwjtQxK1mrzzTf0PvjgpLhOCYuIiDQfFYOSldQvUEREpGWoGJSskqoIXPLJJ3hduzZ6XYsWrWXChDlUVW2kqKgDkyYNo6ysS3OkKSIikjPUZ1CyQklpaVIhuP6ss6iIRJpUCAJMmDCHuXMrWbBgHXPnVjJ+/OzmSFVERCSnqGVQQtVuzhyKf/KTpHhznBKuqtpY77SIiIioGJQQZbpfYFFRBxYsWBc3LSIiIvFUDEqLS1kELloEhc37dpw0aRjjx8+O6zMoIiIi8VQMSpM0ZXBGqiLw2xtv5LsxYzKSY1lZF6ZOHZWRdYuIiOQKDSCRJmnM4IxO//hHnaeEM1UIioiISHrUMihNktbgjOpqSnbcMSm8/9CH/BbFE6brci8iIiIhU8ugNEniYIzE6ZLS0qRCsCISYf+hD+lyLyIiIllExaA0yaRJwxg6tJgBA7owdGhxdHBGqusFVj7xRHSUsC73IiIikl10mliaJHFwRtf//V86T56cNN/+Qx9iUr+9KQumdbkXERGR7KJiULaJWb2avnvskRznFv9JcCq4tnDU5V5ERESyi4pBabTay8q8O/fspNcqIhGGD58KMa1/saeCdbkXERGR7KJiUBrt4GG78W5CbNkbb1DTvz+gU8EiIiKtiQaQSNqKzjoraXDISwxkpwF3RwtBqHtwiYiIiGQftQxKg9osXkzvgw5Kitf2Cxya0PKnU8EiIiKth4pBqVeqO4e8Oedzxo+fzQANAhEREWn1VAxKSqmKwCWff47XpQtloJY/ERGRHKE+gxKnz557JhWCa379ayoiEbwuum2ciIhIrlHLoADQ7p13KD7xxKR47Z1DREREJDepGMwztdcIjL3o80EH75o0n4pAERGR/KBiMI8sWrSWo456gfXrqwH4esEFcHD8PBXffANt2oSQnYiIiIRBxWAemTBhDuvXV+NxWdJrVXffzcYTTgghKxEREQmTisE8UHtqeMB7s3iXKUmv65SwiIhI/lIxmAcuHv8G7847NynepfPtzJhxHGUh5CQiIo8MtB8AAAzqSURBVCLZQcVgjkkcIPLu3LN5O2GetoW3ss8+PZgxaRhlZbpcjIiISD5TMZhjJkyYw9y5lX6/wAXxr+3Or/mM3gzdp4cuGi0iIiKAisGc84PFc3iXe+NilaN+xDErT2FT1UaG6vZxIiIiEkPFYI4wGzbQd+BA7k6I7z/0IaY+NIqpoWQlIiIi2U7FYA5IdR/hnQbcHb2otIiIiEhdVAy2YqmKwCUff4zXrRuvh5CPiIiItD4FYScg6Vm0aC0nnDCd4cOn8s8DL00qBL+9+WYqIhG8bt1CylBERERaI7UMZrnaS8V89FEV229aw3KuTZpHF40WERGRplIxmKVii8BNm7amvIWcikARERHZVioGs8zs2UsZN+5V1q+vBkhZBLbnBgYP7aMRwiIiIrLN8r4YtNYeA9wBtAHud87dGFYur776DaeeOhOAfSlnHnfEvX4Q4/lv+wEM3qtIo4RFRESkWeT1ABJrbRvgLuBYYA/gdGvtHmHlc9JJz9GFjXhcFlcIPsJ+GG6heuhQZs36EVOnjtJt5ERERKRZ5HvL4AHAfOfc1wDW2seB0cAnYSSzx4aFvMWfo9N/4jB+zY8B2HPPbrqFnIiIiDS7fC8GS4HFMdPlwIGxM1hrzwfOB3DOUVxcnLFklncohvWwnM705veAoW/fjuy4Y1cmTz6a4uKuGdu2+AoLCzN6jKV+2v/h0zEIn45BuPJx/+d7MWhSxLzYCefcvRC92a9XWVmZsWTue+p0upzYmU2baujcvg0PP3w4Bx3UO3h1C5nctviKi4u1n0Ok/R8+HYPw6RiEK5f2f0lJSVrz5XsxWA7sEDPdD6gIKRcOO6wfX3xhw9q8iIiI5KF8LwbfAQZZawcAEeA04GfhpiQiIiLScvJ6NLFzrhoYD0wDPvVD7uNwsxIRERFpOfneMohz7nng+bDzEBEREQlDXrcMioiI/P/27j/IrvGO4/h7JUQIIiGiSZSpDEJbvwYzWtUUjdBIVb/VIdKSUrTVUlSpjB81YZSkQxlExVTxES1BKkWUaqlfE21EO40fqUgqTSQRPyZpYvvH81y91u7dvWuz9+49n9fMmb3nueec59zvs/Psd5/znHvMis7JoJmZmVmBORk0MzMzKzAng2ZmZmYF5mTQzMzMrMCcDJqZmZkVmJNBMzMzswJzMmhmZmZWYE4GzczMzAqsqbm5udbn0JM4WGZmZtaTNLW3gUcGq9O0PpeIeGZ91+HFbVDPi+Nf+8VtUPvFbeD4d/HSLieDZmZmZgXmZNDMzMyswJwM1pfran0C5jaoMce/9twGtec2qK3Cxd83kJiZmZkVmEcGzczMzAqsd61PwJKIGAVMAXoBN0iaVONTqnsRcSNwOLBE0m65bABwO7A98AoQkpZHRBMpvqOBd4BvSHo27zMeOC8f9mJJ03L5XsBNQF9gJnCapObO1NGIImIYcDMwGHgPuE7SFLdB94mIjYFHgT6k/ny6pIkRsQNwGzAAeBYYJ2lNRPQhtdlewDLga5Jeycc6BzgBWAd8T9KsXN5q39SZOhpVRPQCngZek3S449+9IuIVYBUpdmsl7e1+qDoeGawDuSO5GjgUGAF8PSJG1PaseoSbgFEtyn4EPCRpOPBQXocU2+F5ORG4Bt5PHicC+wL7ABMjYsu8zzV529J+ozpTRwNbC5whaRdgP+DU/HvrNug+q4GRkj4N7A6Mioj9gEuBK3N8lpOSDPLP5ZJ2BK7M25Hb7WhgV1KMfxERvdrpm6qqo8GdBrxQtu74d7/PS9pd0t553f1QFZwM1od9gPmSXpK0hvTf3hE1Pqe6J+lR4I0WxUcA0/LracDYsvKbJTVLegLoHxHbAl8EHpD0hqTlwAOkP6jbAptLelxSM+k/7bGdrKMhSVpc+m9X0irSH8MhuA26Tf6cb+XVDfPSDIwEpufylvEpxW068IU8inEEcJuk1ZJeBuaT+qVW+6a8T7V1NKSIGAocBtyQ1zsTG8e/67kfqoKTwfowBHi1bH1hLrPqbSNpMaRkBRiUy9uKcaXyha2Ud6aOhhcR2wN7AH/BbdCt8gjSHGAJ6Q/Yi8AKSWvzJuUxeD8++f2VwECqb5uBnaijUU0GziJNlYDOxcbx/2iagd9HxDMRcWIucz9UBSeD9aG1/9p8m3fXaivG1ZZ3po6GFhH9gDuB70t6s8KmboP1QNI6SbsDQ0kjSbu0slkpBl3VBpXiXJg2iIjSnOVnyoo7ExvH/6PZX9KepMuzp0bEARW2dT/UCieD9WEhMKxsfSiwqEbn0tO9XhqOzz+X5PK2YlypfGgr5Z2po2FFxIakRPAWSb/JxW6DGpC0AvgDaf5m/4go3SBYHoP345Pf34I01aLatlnaiToa0f7AmHwDw22kS7eTcfy7laRF+ecS4Lekf4rcD1XByWB9eAoYHhE7RMRGpInEM2p8Tj3VDGB8fj0euLus/LiIaMoT7FfmYf1ZwCERsWWeLHwIMCu/tyoi9svzbY5rcaxq6mhIOS5TgRckXVH2ltugm0TE1hHRP7/uCxxEmrv5MHBU3qxlfEpxOwqYnedBzQCOjog++S7V4cCTtNE35X2qraPhSDpH0lBJ25NiM1vSMTj+3SYiNo2IzUqvSf3HXNwPVcXJYB3I8zq+Q/plfCEV6fnanlX9i4hbgceBnSJiYUScAEwCDo6IfwIH53VIXwfwEmli9vXAKQCS3gAuInW6TwEX5jKAk0mTwueT5mH9LpdXVUcD2x8YB4yMiDl5GY3boDttCzwcEX8lxe4BSfcCZwOnR8R80nyxqXn7qcDAXH46+e7H3N8ImAfcD5yaLz9X6puqqqNgHP/usw3wWEQ8R0qg75N0P+6HquInkJiZmZkVmEcGzczMzArMyaCZmZlZgTkZNDMzMyswJ4NmZmZmBeZk0MzMzKzAere/iZmZlUTExsC7wDBJC9vbfj2exxPAVZJ+VatzMLPG4GTQzHq8iHirbHUTYDWwLq+fJOmWCvuOIiVVO3bh+Ywmfb/bHsAaYAHwa+DnktZ0VT1dYX18fjPrWZwMmlmPJ6lf6XV+NNgESQ/W4lwi4ljgKuAHwBhJKyJiBPBdYDDwr1b26Z2/YNjMrNs5GTSzhpcf1XY5cCRpxPBW4MfA5qRnmfYpG138OLAzcEX++TZwO3Bmewlbfhbs5cC5kn5ZKpc0j/QUg9J2k4DtgF7AaODkiHi5Up0RcRjpubdbAze2qHcSsJWkCXl9Z2CupN55/SRScjoEeB24RNKNETGwjc+/IsfnmzlGs0hPxViRH/k1lfS4rg2AfwCjJC2vFBszq1++gcTMiuAC4FPAJ4G9gAOBsyQtA74MvCSpX16WAf8lPQZsAPBZ4EvAhA7Usxvp8Vh3dmDbrwDTgC3y9m3WGRGDSY8rO4OUDP4H2LsDdZQsBg4lJXbfBq6OiF0rfP4zScneZ4Ch+dyuzMeaQBpIGAJslc+5ri59m1l1PDJoZkVwDDBO0lKAiLgYuBT4aWsbS3qybPXFiLgB+BxwbTv1bAU0A0tKBRFxFyn53AgYL+mO/NYjkmbm1++SnqvaVp1jgKckzcjHvIw00tchpf2yByPiEVKi19Yz0E8CjpW0KNd3AfB8RBxPSgy3Bj4haS7pOa5m1oM5GTSzhhYRTaS5egvKiheQRrba2mcE8DNgT6Avqa/8UweqWwY0kUYHFwNIGpuP+TTpsnDJq1XU+bHy7SWti4jXOnA+pWOPAc4DdiRdEdoE+GMb2zYBw4CZEVH+8PoNgIGkS8SDgekR0Q+4GfiJpHUfOpiZ9Qi+TGxmDU1SM/Bv0ly4ku2AUjLV/KGd4HrgWdLo1+bAhaQkrz1/I40KHtmBbVvWW6nOxaQEDYCI2IAPJrNvkxK8ksFl224K3AFcBAyS1B+YXXbsD5xHjtdrwEhJ/cuWjSUtlbRa0vmSdgYOAL4KHN2Bz2tmdcojg2ZWBLcCEyPiOdLo3LlA6fv5XgcGRUQ/SaWbKDYDVkp6KyJ2Bb4FvNxeJZLWRsRZwJSIeId0c8ZKYCfSJeRKKtU5A7giIg4n3cxxOmluYckc4JSIGAK8A5xd9l5fYENSkvpeHiU8EHiswue/FpgUEcdLejUiBgH7SronIg4CFgF/B94E1vL/r/Exsx7II4NmVgTnA/NIc+TmkC6/Xpbfe46UbC2IiBURMYA0H29CvsP2atKdvR0iaRowDjgeWAgsJX3H4JRcT1varFPSYtLo22TSzSPbAE+X7XsfcG/+jE8Ad5XtuxT4IXAP6TL2WGBm2b6tff7LgAeB2RGxCvgz6fI1pBHJu4FVwNx8LLUfGTOrV03Nza1dITEzMzOzIvDIoJmZmVmBORk0MzMzKzAng2ZmZmYF5mTQzMzMrMCcDJqZmZkVmJNBMzMzswJzMmhmZmZWYE4GzczMzArMyaCZmZlZgf0PInw+F2S07CsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. female graduates = 0 * Year + -5233'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fgd.plot(kind='scatter', x='total_graduates', y='female_graduates', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Number of female Graduates from total graduates')\n",
    "plt.xlabel('Total Graduates')\n",
    "plt.ylabel('Female Graduates')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(1000000,1500000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print out the line of best fit\n",
    "'No. female graduates = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>population_male</th>\n",
       "      <th>population_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53247</td>\n",
       "      <td>47361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9192368</td>\n",
       "      <td>8979247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>720132</td>\n",
       "      <td>671022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3558722</td>\n",
       "      <td>3155794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>525226</td>\n",
       "      <td>435561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population_male  population_female\n",
       "0            53247              47361\n",
       "1          9192368            8979247\n",
       "2           720132             671022\n",
       "3          3558722            3155794\n",
       "4           525226             435561"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfp = lis.loc[:,['population_male', 'population_female']]\n",
    "mfp.set_index('population_male',inplace=True)\n",
    "mfp.reset_index(inplace=True)\n",
    "mfp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mfp.plot(kind='scatter', x='population_male', y='population_female', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Number of Male & Female Population')\n",
    "plt.xlabel('Population Male')\n",
    "plt.ylabel('Population Female')\n",
    "\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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}