Uttarakhand SIR Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing Uttarakhand COVID19 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000020960FF3F48>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"covid_19_india.csv\")\n",
    "df.groupby(by='State/UnionTerritory')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sno</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>State/UnionTerritory</th>\n",
       "      <th>ConfirmedIndianNational</th>\n",
       "      <th>ConfirmedForeignNational</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30/01/2020</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1/2/2020</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2/2/2020</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
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       "      <td>2986</td>\n",
       "      <td>2986</td>\n",
       "      <td>2987</td>\n",
       "      <td>13/06/2020</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>278</td>\n",
       "      <td>1</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2987</td>\n",
       "      <td>2987</td>\n",
       "      <td>2988</td>\n",
       "      <td>13/06/2020</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>947</td>\n",
       "      <td>21</td>\n",
       "      <td>1724</td>\n",
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       "      <td>2988</td>\n",
       "      <td>2988</td>\n",
       "      <td>2989</td>\n",
       "      <td>13/06/2020</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>-</td>\n",
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       "      <td>7609</td>\n",
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       "      <td>2990</td>\n",
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       "      <td>8:00 AM</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4206</td>\n",
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       "<p>2991 rows × 10 columns</p>\n",
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      "text/plain": [
       "      Unnamed: 0   Sno        Date     Time              State/UnionTerritory  \\\n",
       "0              0     1  30/01/2020  6:00 PM                            Kerala   \n",
       "1              1     2  31/01/2020  6:00 PM                            Kerala   \n",
       "2              2     3    1/2/2020  6:00 PM                            Kerala   \n",
       "3              3     4    2/2/2020  6:00 PM                            Kerala   \n",
       "4              4     5    3/2/2020  6:00 PM                            Kerala   \n",
       "...          ...   ...         ...      ...                               ...   \n",
       "2986        2986  2987  13/06/2020  8:00 AM                           Tripura   \n",
       "2987        2987  2988  13/06/2020  8:00 AM                       Uttarakhand   \n",
       "2988        2988  2989  13/06/2020  8:00 AM                     Uttar Pradesh   \n",
       "2989        2989  2990  13/06/2020  8:00 AM                       West Bengal   \n",
       "2990        2990  2991  13/06/2020  8:00 AM  Cases being reassigned to states   \n",
       "\n",
       "     ConfirmedIndianNational ConfirmedForeignNational  Cured  Deaths  \\\n",
       "0                          1                        0      0       0   \n",
       "1                          1                        0      0       0   \n",
       "2                          2                        0      0       0   \n",
       "3                          3                        0      0       0   \n",
       "4                          3                        0      0       0   \n",
       "...                      ...                      ...    ...     ...   \n",
       "2986                       -                        -    278       1   \n",
       "2987                       -                        -    947      21   \n",
       "2988                       -                        -   7609     365   \n",
       "2989                       -                        -   4206     451   \n",
       "2990                       -                        -      0       0   \n",
       "\n",
       "      Confirmed  \n",
       "0             1  \n",
       "1             1  \n",
       "2             2  \n",
       "3             3  \n",
       "4             3  \n",
       "...         ...  \n",
       "2986        961  \n",
       "2987       1724  \n",
       "2988      12616  \n",
       "2989      10244  \n",
       "2990       7984  \n",
       "\n",
       "[2991 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "UK = df[df['State/UnionTerritory'] == 'Uttarakhand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>2843</td>\n",
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       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
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       "      Unnamed: 0   Sno        Date     Time State/UnionTerritory  \\\n",
       "161          161   162  15/03/2020  6:00 PM          Uttarakhand   \n",
       "176          176   177  16/03/2020  6:00 PM          Uttarakhand   \n",
       "191          191   192  17/03/2020  6:00 PM          Uttarakhand   \n",
       "207          207   208  18/03/2020  6:00 PM          Uttarakhand   \n",
       "226          226   227  19/03/2020  6:00 PM          Uttarakhand   \n",
       "...          ...   ...         ...      ...                  ...   \n",
       "2842        2842  2843    9/6/2020  8:00 AM          Uttarakhand   \n",
       "2878        2878  2879   10/6/2020  8:00 AM          Uttarakhand   \n",
       "2915        2915  2916   11/6/2020  8:00 AM          Uttarakhand   \n",
       "2951        2951  2952   12/6/2020  8:00 AM          Uttarakhand   \n",
       "2987        2987  2988  13/06/2020  8:00 AM          Uttarakhand   \n",
       "\n",
       "     ConfirmedIndianNational ConfirmedForeignNational  Cured  Deaths  \\\n",
       "161                        1                        0      0       0   \n",
       "176                        1                        0      0       0   \n",
       "191                        1                        0      0       0   \n",
       "207                        1                        0      0       0   \n",
       "226                        1                        0      0       0   \n",
       "...                      ...                      ...    ...     ...   \n",
       "2842                       -                        -    714      13   \n",
       "2878                       -                        -    755      13   \n",
       "2915                       -                        -    831      15   \n",
       "2951                       -                        -    842      15   \n",
       "2987                       -                        -    947      21   \n",
       "\n",
       "      Confirmed  \n",
       "161           1  \n",
       "176           1  \n",
       "191           1  \n",
       "207           1  \n",
       "226           1  \n",
       "...         ...  \n",
       "2842       1411  \n",
       "2878       1537  \n",
       "2915       1562  \n",
       "2951       1643  \n",
       "2987       1724  \n",
       "\n",
       "[91 rows x 10 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ak.shah\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "UK['Date'] = pd.to_datetime(UK['Date'], format='%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 91 entries, 161 to 2987\n",
      "Data columns (total 10 columns):\n",
      "Unnamed: 0                  91 non-null int64\n",
      "Sno                         91 non-null int64\n",
      "Date                        91 non-null datetime64[ns]\n",
      "Time                        91 non-null object\n",
      "State/UnionTerritory        91 non-null object\n",
      "ConfirmedIndianNational     91 non-null object\n",
      "ConfirmedForeignNational    91 non-null object\n",
      "Cured                       91 non-null int64\n",
      "Deaths                      91 non-null int64\n",
      "Confirmed                   91 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(5), object(4)\n",
      "memory usage: 7.8+ KB\n"
     ]
    }
   ],
   "source": [
    "UK.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ak.shah\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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   ],
   "source": [
    "UK.sort_values(by='Date',inplace=True)\n",
    "UK.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "    .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>Unnamed: 0</th>\n",
       "      <th>Sno</th>\n",
       "      <th>Time</th>\n",
       "      <th>State/UnionTerritory</th>\n",
       "      <th>ConfirmedIndianNational</th>\n",
       "      <th>ConfirmedForeignNational</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <td>2020-03-15</td>\n",
       "      <td>161</td>\n",
       "      <td>162</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>176</td>\n",
       "      <td>177</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>191</td>\n",
       "      <td>192</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>207</td>\n",
       "      <td>208</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>226</td>\n",
       "      <td>227</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>2842</td>\n",
       "      <td>2843</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>714</td>\n",
       "      <td>13</td>\n",
       "      <td>1411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>2878</td>\n",
       "      <td>2879</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>755</td>\n",
       "      <td>13</td>\n",
       "      <td>1537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-06-11</td>\n",
       "      <td>2915</td>\n",
       "      <td>2916</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>831</td>\n",
       "      <td>15</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-06-12</td>\n",
       "      <td>2951</td>\n",
       "      <td>2952</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>842</td>\n",
       "      <td>15</td>\n",
       "      <td>1643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>2987</td>\n",
       "      <td>2988</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>947</td>\n",
       "      <td>21</td>\n",
       "      <td>1724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0   Sno     Time State/UnionTerritory  \\\n",
       "Date                                                         \n",
       "2020-03-15         161   162  6:00 PM          Uttarakhand   \n",
       "2020-03-16         176   177  6:00 PM          Uttarakhand   \n",
       "2020-03-17         191   192  6:00 PM          Uttarakhand   \n",
       "2020-03-18         207   208  6:00 PM          Uttarakhand   \n",
       "2020-03-19         226   227  6:00 PM          Uttarakhand   \n",
       "...                ...   ...      ...                  ...   \n",
       "2020-06-09        2842  2843  8:00 AM          Uttarakhand   \n",
       "2020-06-10        2878  2879  8:00 AM          Uttarakhand   \n",
       "2020-06-11        2915  2916  8:00 AM          Uttarakhand   \n",
       "2020-06-12        2951  2952  8:00 AM          Uttarakhand   \n",
       "2020-06-13        2987  2988  8:00 AM          Uttarakhand   \n",
       "\n",
       "           ConfirmedIndianNational ConfirmedForeignNational  Cured  Deaths  \\\n",
       "Date                                                                         \n",
       "2020-03-15                       1                        0      0       0   \n",
       "2020-03-16                       1                        0      0       0   \n",
       "2020-03-17                       1                        0      0       0   \n",
       "2020-03-18                       1                        0      0       0   \n",
       "2020-03-19                       1                        0      0       0   \n",
       "...                            ...                      ...    ...     ...   \n",
       "2020-06-09                       -                        -    714      13   \n",
       "2020-06-10                       -                        -    755      13   \n",
       "2020-06-11                       -                        -    831      15   \n",
       "2020-06-12                       -                        -    842      15   \n",
       "2020-06-13                       -                        -    947      21   \n",
       "\n",
       "            Confirmed  \n",
       "Date                   \n",
       "2020-03-15          1  \n",
       "2020-03-16          1  \n",
       "2020-03-17          1  \n",
       "2020-03-18          1  \n",
       "2020-03-19          1  \n",
       "...               ...  \n",
       "2020-06-09       1411  \n",
       "2020-06-10       1537  \n",
       "2020-06-11       1562  \n",
       "2020-06-12       1643  \n",
       "2020-06-13       1724  \n",
       "\n",
       "[91 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "UK = UK.drop([\"Unnamed: 0\", \"Sno\", \"Time\",\"State/UnionTerritory\",\"ConfirmedIndianNational\",\"ConfirmedForeignNational\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20962589788>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "UK.plot(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIR Modeling for Uttarakhand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We need population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = pd.read_csv(\"population_india_census2011.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Sno</th>\n",
       "      <th>State / Union Territory</th>\n",
       "      <th>Population</th>\n",
       "      <th>Rural population</th>\n",
       "      <th>Urban population</th>\n",
       "      <th>Area</th>\n",
       "      <th>Density</th>\n",
       "      <th>Gender Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>199812341</td>\n",
       "      <td>155317278</td>\n",
       "      <td>44495063</td>\n",
       "      <td>240,928 km2 (93,023 sq mi)</td>\n",
       "      <td>828/km2 (2,140/sq mi)</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>112374333</td>\n",
       "      <td>61556074</td>\n",
       "      <td>50818259</td>\n",
       "      <td>307,713 km2 (118,809 sq mi)</td>\n",
       "      <td>365/km2 (950/sq mi)</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>104099452</td>\n",
       "      <td>92341436</td>\n",
       "      <td>11758016</td>\n",
       "      <td>94,163 km2 (36,357 sq mi)</td>\n",
       "      <td>1,102/km2 (2,850/sq mi)</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>91276115</td>\n",
       "      <td>62183113</td>\n",
       "      <td>29093002</td>\n",
       "      <td>88,752 km2 (34,267 sq mi)</td>\n",
       "      <td>1,029/km2 (2,670/sq mi)</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72626809</td>\n",
       "      <td>52557404</td>\n",
       "      <td>20069405</td>\n",
       "      <td>308,245 km2 (119,014 sq mi)</td>\n",
       "      <td>236/km2 (610/sq mi)</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>72147030</td>\n",
       "      <td>37229590</td>\n",
       "      <td>34917440</td>\n",
       "      <td>130,058 km2 (50,216 sq mi)</td>\n",
       "      <td>555/km2 (1,440/sq mi)</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>68548437</td>\n",
       "      <td>51500352</td>\n",
       "      <td>17048085</td>\n",
       "      <td>342,239 km2 (132,139 sq mi)</td>\n",
       "      <td>201/km2 (520/sq mi)</td>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>61095297</td>\n",
       "      <td>37469335</td>\n",
       "      <td>23625962</td>\n",
       "      <td>191,791 km2 (74,051 sq mi)</td>\n",
       "      <td>319/km2 (830/sq mi)</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>60439692</td>\n",
       "      <td>34694609</td>\n",
       "      <td>25745083</td>\n",
       "      <td>196,024 km2 (75,685 sq mi)</td>\n",
       "      <td>308/km2 (800/sq mi)</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>49577103</td>\n",
       "      <td>34966693</td>\n",
       "      <td>14610410</td>\n",
       "      <td>162,968 km2 (62,922 sq mi)</td>\n",
       "      <td>303/km2 (780/sq mi)</td>\n",
       "      <td>993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>41974218</td>\n",
       "      <td>34970562</td>\n",
       "      <td>7003656</td>\n",
       "      <td>155,707 km2 (60,119 sq mi)</td>\n",
       "      <td>269/km2 (700/sq mi)</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>Telengana</td>\n",
       "      <td>35003674</td>\n",
       "      <td>21395009</td>\n",
       "      <td>13608665</td>\n",
       "      <td>112,077 km2 (43,273 sq mi)</td>\n",
       "      <td>312/km2 (810/sq mi)</td>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>33406061</td>\n",
       "      <td>17471135</td>\n",
       "      <td>15934926</td>\n",
       "      <td>38,863 km2 (15,005 sq mi)</td>\n",
       "      <td>859/km2 (2,220/sq mi)</td>\n",
       "      <td>1084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>32988134</td>\n",
       "      <td>25055073</td>\n",
       "      <td>7933061</td>\n",
       "      <td>79,714 km2 (30,778 sq mi)</td>\n",
       "      <td>414/km2 (1,070/sq mi)</td>\n",
       "      <td>948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>Assam</td>\n",
       "      <td>31205576</td>\n",
       "      <td>26807034</td>\n",
       "      <td>4398542</td>\n",
       "      <td>78,438 km2 (30,285 sq mi)</td>\n",
       "      <td>397/km2 (1,030/sq mi)</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>27743338</td>\n",
       "      <td>17344192</td>\n",
       "      <td>10399146</td>\n",
       "      <td>50,362 km2 (19,445 sq mi)</td>\n",
       "      <td>550/km2 (1,400/sq mi)</td>\n",
       "      <td>895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>25545198</td>\n",
       "      <td>19607961</td>\n",
       "      <td>5937237</td>\n",
       "      <td>135,191 km2 (52,198 sq mi)</td>\n",
       "      <td>189/km2 (490/sq mi)</td>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>25351462</td>\n",
       "      <td>16509359</td>\n",
       "      <td>8842103</td>\n",
       "      <td>44,212 km2 (17,070 sq mi)</td>\n",
       "      <td>573/km2 (1,480/sq mi)</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>10086292</td>\n",
       "      <td>7036954</td>\n",
       "      <td>3049338</td>\n",
       "      <td>53,483 km2 (20,650 sq mi)</td>\n",
       "      <td>189/km2 (490/sq mi)</td>\n",
       "      <td>963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>6864602</td>\n",
       "      <td>6176050</td>\n",
       "      <td>688552</td>\n",
       "      <td>55,673 km2 (21,495 sq mi)</td>\n",
       "      <td>123/km2 (320/sq mi)</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>3673917</td>\n",
       "      <td>2712464</td>\n",
       "      <td>961453</td>\n",
       "      <td>10,486 km2 (4,049 sq mi)</td>\n",
       "      <td>350/km2 (910/sq mi)</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>2966889</td>\n",
       "      <td>2371439</td>\n",
       "      <td>595450</td>\n",
       "      <td>22,429 km2 (8,660 sq mi)</td>\n",
       "      <td>132/km2 (340/sq mi)</td>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>2570390</td>\n",
       "      <td>1793875</td>\n",
       "      <td>776515</td>\n",
       "      <td>22,327 km2 (8,621 sq mi)</td>\n",
       "      <td>122/km2 (320/sq mi)</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>1978502</td>\n",
       "      <td>1407536</td>\n",
       "      <td>570966</td>\n",
       "      <td>16,579 km2 (6,401 sq mi)</td>\n",
       "      <td>119/km2 (310/sq mi)</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>Goa</td>\n",
       "      <td>1458545</td>\n",
       "      <td>551731</td>\n",
       "      <td>906814</td>\n",
       "      <td>3,702 km2 (1,429 sq mi)</td>\n",
       "      <td>394/km2 (1,020/sq mi)</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>1383727</td>\n",
       "      <td>1066358</td>\n",
       "      <td>317369</td>\n",
       "      <td>83,743 km2 (32,333 sq mi)</td>\n",
       "      <td>17/km2 (44/sq mi)</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>1097206</td>\n",
       "      <td>525435</td>\n",
       "      <td>571771</td>\n",
       "      <td>21,081 km2 (8,139 sq mi)</td>\n",
       "      <td>52/km2 (130/sq mi)</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>610577</td>\n",
       "      <td>456999</td>\n",
       "      <td>153578</td>\n",
       "      <td>7,096 km2 (2,740 sq mi)</td>\n",
       "      <td>86/km2 (220/sq mi)</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>16787941</td>\n",
       "      <td>419042</td>\n",
       "      <td>16368899</td>\n",
       "      <td>1,484 km2 (573 sq mi)</td>\n",
       "      <td>11,297/km2 (29,260/sq mi)</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>12267032</td>\n",
       "      <td>9064220</td>\n",
       "      <td>3202812</td>\n",
       "      <td>125,535 km2 (48,469 sq mi)</td>\n",
       "      <td>98/km2 (250/sq mi)</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>Puducherry</td>\n",
       "      <td>1247953</td>\n",
       "      <td>395200</td>\n",
       "      <td>852753</td>\n",
       "      <td>479 km2 (185 sq mi)</td>\n",
       "      <td>2,598/km2 (6,730/sq mi)</td>\n",
       "      <td>1037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>1055450</td>\n",
       "      <td>28991</td>\n",
       "      <td>1026459</td>\n",
       "      <td>114 km2 (44 sq mi)</td>\n",
       "      <td>9,252/km2 (23,960/sq mi)</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>585764</td>\n",
       "      <td>243510</td>\n",
       "      <td>342254</td>\n",
       "      <td>603 km2 (233 sq mi)</td>\n",
       "      <td>970/km2 (2,500/sq mi)</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>380581</td>\n",
       "      <td>237093</td>\n",
       "      <td>143488</td>\n",
       "      <td>8,249 km2 (3,185 sq mi)</td>\n",
       "      <td>46/km2 (120/sq mi)</td>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>274000</td>\n",
       "      <td>43840</td>\n",
       "      <td>230160</td>\n",
       "      <td>96,701 km2 (37,336 sq mi)</td>\n",
       "      <td>2.8/km2 (7.3/sq mi)</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>Lakshadweep</td>\n",
       "      <td>64473</td>\n",
       "      <td>14141</td>\n",
       "      <td>50332</td>\n",
       "      <td>32 km2 (12 sq mi)</td>\n",
       "      <td>2,013/km2 (5,210/sq mi)</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sno                   State / Union Territory  Population  \\\n",
       "0     1                             Uttar Pradesh   199812341   \n",
       "1     2                               Maharashtra   112374333   \n",
       "2     3                                     Bihar   104099452   \n",
       "3     4                               West Bengal    91276115   \n",
       "4     5                            Madhya Pradesh    72626809   \n",
       "5     6                                Tamil Nadu    72147030   \n",
       "6     7                                 Rajasthan    68548437   \n",
       "7     8                                 Karnataka    61095297   \n",
       "8     9                                   Gujarat    60439692   \n",
       "9    10                            Andhra Pradesh    49577103   \n",
       "10   11                                    Odisha    41974218   \n",
       "11   12                                 Telengana    35003674   \n",
       "12   13                                    Kerala    33406061   \n",
       "13   14                                 Jharkhand    32988134   \n",
       "14   15                                     Assam    31205576   \n",
       "15   16                                    Punjab    27743338   \n",
       "16   17                              Chhattisgarh    25545198   \n",
       "17   18                                   Haryana    25351462   \n",
       "18   19                               Uttarakhand    10086292   \n",
       "19   20                          Himachal Pradesh     6864602   \n",
       "20   21                                   Tripura     3673917   \n",
       "21   22                                 Meghalaya     2966889   \n",
       "22   23                                   Manipur     2570390   \n",
       "23   24                                  Nagaland     1978502   \n",
       "24   25                                       Goa     1458545   \n",
       "25   26                         Arunachal Pradesh     1383727   \n",
       "26   27                                   Mizoram     1097206   \n",
       "27   28                                    Sikkim      610577   \n",
       "28   29                                     Delhi    16787941   \n",
       "29   30                         Jammu and Kashmir    12267032   \n",
       "30   31                                Puducherry     1247953   \n",
       "31   32                                Chandigarh     1055450   \n",
       "32   33  Dadra and Nagar Haveli and Daman and Diu      585764   \n",
       "33   34               Andaman and Nicobar Islands      380581   \n",
       "34   35                                    Ladakh      274000   \n",
       "35   36                               Lakshadweep       64473   \n",
       "\n",
       "    Rural population  Urban population                         Area  \\\n",
       "0          155317278          44495063   240,928 km2 (93,023 sq mi)   \n",
       "1           61556074          50818259  307,713 km2 (118,809 sq mi)   \n",
       "2           92341436          11758016    94,163 km2 (36,357 sq mi)   \n",
       "3           62183113          29093002    88,752 km2 (34,267 sq mi)   \n",
       "4           52557404          20069405  308,245 km2 (119,014 sq mi)   \n",
       "5           37229590          34917440   130,058 km2 (50,216 sq mi)   \n",
       "6           51500352          17048085  342,239 km2 (132,139 sq mi)   \n",
       "7           37469335          23625962   191,791 km2 (74,051 sq mi)   \n",
       "8           34694609          25745083   196,024 km2 (75,685 sq mi)   \n",
       "9           34966693          14610410   162,968 km2 (62,922 sq mi)   \n",
       "10          34970562           7003656   155,707 km2 (60,119 sq mi)   \n",
       "11          21395009          13608665   112,077 km2 (43,273 sq mi)   \n",
       "12          17471135          15934926    38,863 km2 (15,005 sq mi)   \n",
       "13          25055073           7933061    79,714 km2 (30,778 sq mi)   \n",
       "14          26807034           4398542    78,438 km2 (30,285 sq mi)   \n",
       "15          17344192          10399146    50,362 km2 (19,445 sq mi)   \n",
       "16          19607961           5937237   135,191 km2 (52,198 sq mi)   \n",
       "17          16509359           8842103    44,212 km2 (17,070 sq mi)   \n",
       "18           7036954           3049338    53,483 km2 (20,650 sq mi)   \n",
       "19           6176050            688552    55,673 km2 (21,495 sq mi)   \n",
       "20           2712464            961453     10,486 km2 (4,049 sq mi)   \n",
       "21           2371439            595450     22,429 km2 (8,660 sq mi)   \n",
       "22           1793875            776515     22,327 km2 (8,621 sq mi)   \n",
       "23           1407536            570966     16,579 km2 (6,401 sq mi)   \n",
       "24            551731            906814      3,702 km2 (1,429 sq mi)   \n",
       "25           1066358            317369    83,743 km2 (32,333 sq mi)   \n",
       "26            525435            571771     21,081 km2 (8,139 sq mi)   \n",
       "27            456999            153578      7,096 km2 (2,740 sq mi)   \n",
       "28            419042          16368899        1,484 km2 (573 sq mi)   \n",
       "29           9064220           3202812   125,535 km2 (48,469 sq mi)   \n",
       "30            395200            852753          479 km2 (185 sq mi)   \n",
       "31             28991           1026459           114 km2 (44 sq mi)   \n",
       "32            243510            342254          603 km2 (233 sq mi)   \n",
       "33            237093            143488      8,249 km2 (3,185 sq mi)   \n",
       "34             43840            230160    96,701 km2 (37,336 sq mi)   \n",
       "35             14141             50332            32 km2 (12 sq mi)   \n",
       "\n",
       "                      Density  Gender Ratio  \n",
       "0       828/km2 (2,140/sq mi)           912  \n",
       "1         365/km2 (950/sq mi)           929  \n",
       "2     1,102/km2 (2,850/sq mi)           918  \n",
       "3     1,029/km2 (2,670/sq mi)           953  \n",
       "4         236/km2 (610/sq mi)           931  \n",
       "5       555/km2 (1,440/sq mi)           996  \n",
       "6         201/km2 (520/sq mi)           928  \n",
       "7         319/km2 (830/sq mi)           973  \n",
       "8         308/km2 (800/sq mi)           919  \n",
       "9         303/km2 (780/sq mi)           993  \n",
       "10        269/km2 (700/sq mi)           979  \n",
       "11        312/km2 (810/sq mi)           988  \n",
       "12      859/km2 (2,220/sq mi)          1084  \n",
       "13      414/km2 (1,070/sq mi)           948  \n",
       "14      397/km2 (1,030/sq mi)           954  \n",
       "15      550/km2 (1,400/sq mi)           895  \n",
       "16        189/km2 (490/sq mi)           991  \n",
       "17      573/km2 (1,480/sq mi)           879  \n",
       "18        189/km2 (490/sq mi)           963  \n",
       "19        123/km2 (320/sq mi)           972  \n",
       "20        350/km2 (910/sq mi)           960  \n",
       "21        132/km2 (340/sq mi)           989  \n",
       "22        122/km2 (320/sq mi)           992  \n",
       "23        119/km2 (310/sq mi)           931  \n",
       "24      394/km2 (1,020/sq mi)           973  \n",
       "25          17/km2 (44/sq mi)           938  \n",
       "26         52/km2 (130/sq mi)           976  \n",
       "27         86/km2 (220/sq mi)           890  \n",
       "28  11,297/km2 (29,260/sq mi)           868  \n",
       "29         98/km2 (250/sq mi)           890  \n",
       "30    2,598/km2 (6,730/sq mi)          1037  \n",
       "31   9,252/km2 (23,960/sq mi)           818  \n",
       "32      970/km2 (2,500/sq mi)           711  \n",
       "33         46/km2 (120/sq mi)           876  \n",
       "34        2.8/km2 (7.3/sq mi)           853  \n",
       "35    2,013/km2 (5,210/sq mi)           946  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From 2011 census data its 10086292\t for Uttarakhand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The SIR model differential equations.\n",
    "def deriv(state, t, N, beta, gamma):\n",
    "    S, I, R = state\n",
    "    # Change in S population over time\n",
    "    dSdt = -beta * S * I / N\n",
    "    # Change in I population over time\n",
    "    dIdt = beta * S * I / N - gamma * I\n",
    "    # Change in R population over time\n",
    "    dRdt = gamma * I\n",
    "    return dSdt, dIdt, dRdt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by modeling our population.\n",
    "\n",
    "Effective contact rate is transmission rate * contact rate, so:\n",
    "\n",
    "For example, 5% transmission rate and 5 contacts a day is 0.05 * 5 = 0.25\n",
    "Recovery rate is 1 / days, so\n",
    "\n",
    "For example, 4 day recovery rate is 1 / 4 = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R0 is 2.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20962b6e508>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FEM1PKVBQ1MsZ7kraJAl6IUTLUIpKj4k3QWKnpckaF0K0HFtTmOYkbGc7aqMk6IUQLcdQ1MQbVLaTHetbkgS9EKJl2ZqiXg60bJdtMRL0QoiWZSgCLkNaI7QgCXohRMvTmuIeDgJOGda3BAl6IUTL238QVWFvOYiqJUjQCyHCprKdRXWSxFBzkzUshAgPpcDWFPRxyobZZiZBL4QIH0Phj5ENs81Ngl4IEV6yYbbZSdALIcJr/4bZgj5yxGxzkaAXQoSfUlSlmlS0lyNmm4MEvRAiMuzvgyMnKGl6EvRCiMhgKGwTCtOklXFTk6AXQkSUynYWlR6ZwmlKEvRCiMixf9/6fX2cBGUKp8lI0AshIouhsC3Y188le+E0kQYF/bp167jzzjv505/+xLx584643LfffsuYMWPYtm1bkxUohGibqlJMyjrLgVRNod6gt22bWbNm8eCDDzJlyhSWLl1KTk7OIctVV1ezaNEi+vbt2yyFCiHakP1TOIW9HPhi5UCq41Vv0GdlZdGpUyc6duyIZVkMGzaMlStXHrLc3Llz+eUvf4nDIRNrQogmYIQCfu9JLmyZZD4u9a6+oqIiPB5P7WWPx0NRUVGdZXbs2EFBQQGDBw8+6mNlZmYyfvx4xo8ff4zlCiHaFEPhjzUo7CUDyONR7wSY1oduDlHqpz+lbNvm1VdfZdy4cfU+WUZGBhkZGY0sUQjRpmlNeWcHrgqbxL3BcFfTKtUb9B6Ph8LCwtrLhYWFpKSk1F72er1kZ2fz+OOPA1BSUsKzzz7LfffdR1paWjOULIRoUw60M05z4qyswV1hh7uiVqfeoE9LSyMvL4/8/HxSU1NZtmwZd9xxR+3tsbGxzJo1q/byY489xm9+8xsJeSFE0zEUKqjZ299F17XVWP5wF9S61Bv0pmkyduxYJkyYgG3bjBw5ku7duzN37lzS0tJIT09viTqFEG2cNhVBQ5N/sovO62tQspN9gyl9uEn4FvLON4fuvSOEEEekNShFwp4A7bJ8tMUdLwf/eUSj7yM7LQkhWg+lQhtnO1mUdJeDqRpKgl4I0brs3zhbfIKTso7S/KwhJOiFEK2PoSAYOrF4VYrEWH1kDQkhWidToezQkbPeRImyo5G1I4RotbQZ2hybN8CFN0Hi7EhkzQghWjVtKtCQN1DC/khkrQghWj1t7R/ZS9gflqwRIURUqJ3GGeiiKlmi7WCyNoQQUUObCqVhT38X5R1k18sDJOiFEFHFthSGHToVYXE3S05HiAS9ECIK2ZZCBWyKezop6ONEt8VeCQeRoBdCRCVtGRAMtUvIPdVFwNl2016CXggRvcxQuwRfrEHO6W6qk9pm5LXNVy2EaDsMVWePnOLubW/eXoJeCNEm2A6FEQg1Q8s9zYXP3XamciTohRBthu0waqdydp/uprRT2xjdS9ALIdqW/VM5RlBT2MdJ7qkuauKie3QvQS+EaJOCTgMV1PhjDXYPclPQy4EdpcdYySlahBBtljYVGjBrbMq6WFS0t0jJ9pO4JxBV56SVEb0Qos0LukJRaNiawjQn2YPdlLc3o2b+XoJeCCEAlCLgDh1khYJ9J7rIOcNNWUez1R9ZK1M3QghxMFMRMBUqoNGmoqCvi+IeNkm5ARL2BjAD4S6w8STohRDiMLSlCFhAUGPYUNTLSXEPB3EFQRL3BHCV27SWgX7EBX2qw8RtmqjWsgYjnNbgDQYp8gfDXYoQrZOp8MeEWilYNTaV7UwqOlo4K23i8wPE7wti+SJ7Nj+igj7VYdI5KRHLiqiyWr1AIAClZRL2QhwPQ+GPDe1/adTYaCM0yi/qqXGX2cTtCxJXFJmhH1GJ6jZNCflmYFkWbtMECXohmoTtMrABtMZRZRNwKwr7OCkEnOWhwI8ptnFVRMb0TkSlqkzXNB9Zt0I0A6Xwx+0/ysoOhb62FMUnOCk+AQy/JqY0SEyJjbssiKNKhyX4IyrohRCi1TIU/vj9oa81plfjCGi8SSaV7UJRa/g17nIbV1kQV0VoxN8Se/FEdNAXfjQFf2FOkz2ew9MNz6V3H3WZa399NW+9+95Rl1m1YgWPPvwQlmUx971/43a7G1xD5qef0rNXL/r07dvg+wCcPnAAazdsbNR9hBBhohTBGEXtZGlQY3ltrCD44hRVqc7aRS2vjbPSxlmpcVbZOKtsHNW6SY/Mjeig9xfm4Mv+vkWfs76QB/jwgw8Y+4ebufqaaxr9+Jn/71POH3lBo4NeCNGKmYpAnEnt4F1rDJ/GUROayvHFGVSlqp/mWLXG8moc1RqH18bh3X/Zax/T00d00IfDgZHz8m+/ZdrfppKSksKWLVsYMPAUnpsyhXffnsvHHy3k66+WsGzpUiZPncr//eMlFi38CJ+vhgsvupg77g791TDv3+8x6+WXUUpx4kkncd0NN/J5ZiYrli/nxenTeGHGiwA8/ugjFBcV4Xa7efLpiaSlpZGdnc29d91JIBBk+IjzwrlKhBBNTSlsl6LGddB1WmP4NWaNjRUAbUAgRuFNsmpPnHKsJOiPYtPGTSz85BM6dOzIddf8mtWrVnHNf13L6lWrOH/kBVxy6aV8/dUSdu3cybvz5qG15tabb2bliuUkJ6fw4vTp/Oudd0lNTaWkpITk5GQuyMiovS/Af99wA48/9RQ9e/Xiu3VrefyRh3ntn28y4YnHue6GGxh91dX887XXwrwmhBDNTilsB9gOE//Pb7M1pk9jBI5tPqdBQb9u3Tpmz56NbduMGjWK0aNH17l9wYIFfPbZZ5imSWJiIrfeeivt27c/poIiyamnnUqnzp0BOOnk/uzenUP6mWfWWWbpV1+x9KuvGH35ZQBUVVWxc8dOvN7NXPKLX5CamgpAcnLyIY9fWVnJ2jWrufP222qv8/l8AKxdvbp2xH/lr37Fc88+0/QvUAjROhiKoPugOf9Gqjfobdtm1qxZPPTQQ3g8Hh544AHS09Pp1q1b7TI9e/Zk4sSJuFwuPv30U9544w3uvvvoGz1bA6fzp7+rTNMgGDh0NWut+eOt47j2+uvrXP/anNn17tOobZvExEQ+WPjRYW9Xsk+kEKIJ1Nu9Misri06dOtGxY0csy2LYsGGsXLmyzjIDBw7E5QqFYt++fSkqKmqeaiPQueedx3vvvE1lZSUAe/fsobCggKHDzuHjhQspLi4GoKSkBIC4uLjaZeMTEujWvTuLPloIhH5p/LB5EwCnDx7MwgXzgdDGXyGEOFb1juiLiorweDy1lz0eD1u3bj3i8p9//jmDBg067G2ZmZlkZmYCMHHixHqLc3i61btMYzT14wGcO/w8tmVt49qrrwYgNi6WSc9PoW+/ftxy2+385rprMQyD/gMGMHHSc1x6+RU8/OADvP7qHP4+fQaTpkzlsYcf4sVp0wgEAlx6+RWcdHJ//vLIo9x71528NnsOF19ySZPXLYRoO5TW+qiz+9988w3fffcdt9xyCwBLliwhKyuLsWPHHrLskiVL+OSTT3jsscdwOBz1Pvk739T9y6BrjJN2SUmNqV80UEFpKburfeEuQwhxnK4Zemb9C/1MvVM3Ho+HwsLC2suFhYWkpKQcstz333/P+++/z3333degkBdCCNEy6g36tLQ08vLyyM/PJxAIsGzZMtLT0+sss2PHDl5++WXuu+8+kmRELoQQEaXeOXrTNBk7diwTJkzAtm1GjhxJ9+7dmTt3LmlpaaSnp/PGG2/g9Xp5/vnnAWjXrh33339/sxcvhBCifvXO0TcnmaNvOTJHL0R0aJY5eiGEEK2bBL0QQkS5iO51k11aRs1hjkY9Vi7LpHtSYpM9XlOZ88or/Nd11xETEwPAzb/7HZP/9jfKysq45Q+/Z8HHnxxyn99cdy33PfAgp5x6akuXK9qCYACzYBtWwXZUdSlGTQXKWw52EO1OxI5JRLsTCSZ3JdChLzhjw12xOIqIDvqaQJBKfwt05Q+z12a/wi9Hj64N+pdnzwagrKwsnGWJNkZVFuHKWoKVuwErfysqGGqtpZWBdsWjnbGgDNS+baiacpQOtczVKIIp3Qh0PBF/j8EEugwEwwznSxE/E9FBHw5VVVXcdfvt7NmTh23bjLv9Tzz3zETe/eBDUlNTWf/99zz79F95/V9vsWL5t0x44gkg1JfmjbfmEh8ff9i2xTk5Ofzhpv/mtEGD2LRxI7169eKZyc/zzty3yM/P57+vv57k1BRef/NfXDD8XN794EMgdGLv++/93zr3OfAL4YCvv1rCC1On4vP56N6jB08/O4m4uLgWX3eiFdI21u71uH74DEf2GtAaO6UrgR6D0ZYLI38rZtkeDG8ZeOsOPLQysd0J6MSOqIAPV9ZXuH/IxHYn4ut1Nr60cwi27yvnsYwAMkf/M199+SUdOnbgw48WseDjTxh+3pF7wb/y8ss88vgTfLDwI/45923cbnedtsUfLPyIjRs2sHLFcgB2bN/OmGuvY/6ij4mLT+DNN17ntzf9jg4dOvDqm2/y+pv/OuQ5DnefgxUVFfHitGnMfv0N3p+/gIGnnMrsWbOadqWI6KM1jv+sJvH9+0n49BmsvT/iTzsHf48zMEp249jxLc6tX2KV5taO3H9O6SBmdQnW3h8xC3egAjXYrgTsOA+uH78gccFjJHz4EM5tX0Mw+v8yj2QS9D/T78QTWbZ0KZMmTmTVihUkJB55Tv+MwelMnPAUr82ZTXlZGZZl1Wlb/KsrLmf79m3s3LETgM6duzB4/8Fmvxw9mtWrVtVbT333+W7tWrKysrjuml9z5WWXMu/f75G7e/cxvnrRFpj7thG/6CniMydD0I+v/8VgOnBmfYXzP6tRx7HHtVFTjlW4A2UHCMYkY3hLiftyBknv3Inr+w/B723CVyIaSqZufqZX7978+8P5fLn4CyZPmsQ5w4djWhbaDo1qampqapf94623MmLkSL5cvJgxV1/F7NffOGLb4pycnEP+gm1IG+L67qPRnHPOuTz/97834lWKtkjVVBCz4k1cWxdjuxPw9b8Ya8cKnJsO3djfFMzqUMdW23KhHTHErnoL9/oFeE+5nJqTLwJHw8+1LI6PjOh/Zu/evcTExHDl6F/x+5tvZtPGDXTt2o0NGzYA8OnHH9cu+59duzjxpJP44y23MPCUU9ixbdsR2xYD5ObmsnbNGgAWzv+wdqQeFxdfu/zPHek+BwwadDprVq9m186dAFRXV7Nj+/YmWhsiWjh2rSLx3/fhzFqCr+8I7FgPzk2fYFQXN/tzG4EazNJctDLBlUDsqrdCI/wNCyEgB/G1hIge0buspt1y35DH2/LjDzz79NMYhoFlOXjsySep8Xr5y/j7eWnGdE47qAXzq7NfYfm332IYBn369OW8ESNwulyHbVtsmCZpffrw/nvv8chfHqRnz15cd8ONAIy57lpu/t1NtO/Q4ZB5+iPd54BUj4enJ03injvvxOcL/bVx1//+L7169z6udSWig6qpIHbZbJw7viGY3I1Az7NwbP5/KFr+gHilg6iyvFDgO2KJXfFPXBs/xnvGNfjSzgVDxp3NRVogtJCcnJwj7hPfEqQFQttj5W0i7ssZKG8pvr7nY2WvxayKnJMCacNExyRjVBYSSOlO9ZnXE+h6quylU49jaYEQ0SN6IcQxsAO417yH+/sPsRM64D/5QpwbPybS4lPZQVRlIbbpwvCWkfDpM/i7DKT6zOsJenqGu7yoIiP6NkJG9G2DUb6PuMUvYO3LwtfzLIzyfKzCneEuq0FsZ1xoV06/F1+fc6k+Yww63lP/HdsYGdEL0YY5dq0k9quXUFpTc8rlODd9Unt0a2tg+EI7JARjU3Bu/wbnjm+p6X8J1addKS0WjpMEvRCtXdBPzMo3cW/6hGBqDwKpJ+BavyDcVR0zs6oYDeiEDrjXz8e55Qu8g35FzYmjwHKGu7xWSYJeiFbMKM0jbvE0rMId+NLOxdi7BWfWV+Eu67gpQJXno1HgjCV2+eu4N3xE9elX4+szXHrpNJIEvRCtlDPra2KXvYI2zNBUzcZFKLvpur1GAoUOBb5hAYq4r/+Be8NCqgddjb/XWaBkl8yGiOigr15ViF3RdHOMRryDmPTo3bhz+sABrN2wMdxliObmryb2m1dxZS0h0L4POt7TqqdqGkLZAZPCaHAAABMFSURBVFRlAbblRPmqiF/8d4Lfdad60FX4e54pgV+PiA56u8KPXVBT/4LNRGuN1hojjAdyBAIBLCui3ybRgqw9PxC75EWMygJ8J16AtWs1xr6scJfVYoyADwI+bEcMqqaC+C/+RiClO95Tr8Tf62yZ0jkCSZCfycnJ4ebf3cTZQ4aybu0aMi68iC8+/7xOy2GAef9+j1kvv4xSihNPOolJz09h9+4cHrzvfoqKCklN9fD0s8+SkJDAlZddSuaXSzAMg+rqai7JGEXm4i/Jy83l8UcfobioCLfbzZNPTyQtLY3xf76XpKRkNm3ayIABA7j+xt8cdrns7GzuvetOAoEgw0ccucumiAJBPzFr3sW1fgE6vh2+/pfg3PTxcTUga80MfzX4q7EdMRg1lcR/OY3gmrfxnnI5vj7nyUbbn5GgP4wd27fz9LOTyLjoQj5ZtIh3581Da82tN9/MyhXLSU5O4cXp0/nXO++SmppKSUmoedOTjz7K6Kuu4ldXX827b7/NU088zoyX/sGJJ5/MiuXLGTJ0KF989hnnDh+Ow+Hg4Qcf5PGnnqJnr158t24tjz/yMK/9800Adu7YwZzX38A0Tf77hhsOu9yEJx7nuhtuYPRVV/PP114L5yoTzcjMzyJu6cuYxdn4ew1Ble3FtXFRuMuKCLWBb7lRQNyyV4hZ8y41J15AzckXomNTwl1iRJCgP4wuXbsy6PTTeeavE2pbDkPopCQ7d+zE693MJb/4BampqQAkJycDsHbtWl54cSYAV/7qV0x6ZiIAl152OR8tWMCQoUNZuGA+199wI5WVlaxds5o7b7+t9nl9vp8OaLrk0ksxTfOoy61dvZoXZrxY+3zPPftMc60SEQ6+KmJWz8W1ORMdm7x/g+vHKFt6u/+cEfBCuRdtONDuBNzffYD7+/n4ep1NzckXEezQtk+AIkF/GLGxoYMzjtRy+LU5sxv0oTnQUviCjAyen/QsJSUlbFy/gSHDhlFdVUViYiIfLPzosPc9cBYpbdtHXa4hrY5FK6M1jh3fErv8dZS3FH/aORgluVG/wbUpKNuPWbIbDdhJnXD+ZzWu7csIJnel5sQL8KWdi3YnhLvMFiebqo/iSC2Hhw47h48XLqS4ONTi9cDUzelnnMHCBfMBmP/BBwe1IY7jlNNOY8ITj3P+BRdgmibxCQl0696dRR8tBEK/VH7YvOmQGo623OmDB9c+34cffNBcq0G0IHPPDyQseJT4xS+g3Qn4T74Qx/alWIXSeroxFGCW7EYFagjGJIMdJHb56yS9dRtxn03BsXMltKKjho9XRI/ojXhHWB/v3OHnHbblcN9+/bjlttv5zXXXYhgG/QcMYOKk53jo0cd48P77mPWPf9RujD3g0ssu587bb+P1f/3UhnjSlKk89vBDvDhtGoFAgEsvv4KTTu5/SB1HWu4vjzzKvXfdyWuz53DxJZcc41oRkcAoziFmzTs4d63EjkmmZsAlOLKW4tz0abhLa/XM6hKoDp3jNpjcBWvP5tB6dsbi73kWvhPOItBlAJhNmzeRRJqatRHS1Cwymfu24f7+Q5y7VqItN/60YRh7f8AqyQ13aVHNdsSikzpilOSiAjVoRwy+7qfj75FOoOtAtCs+3CUekTQ1E6I1sIM4stfi2vwpjtwNaGcsvn7no0rzcP74ebiraxMMfxUU7ADAdsVjJ3bEmbMO1/ZlaGUQ6NCXQLfT8HcZSNDTq9Xvny9BL0QLMSr24dyyGNeWxRhVxdgxyfhOygj1p9myONzltVlGTQXGvgoAbEcMOrkLRnUZMavfJmb122hHDP5OJxHodDKBjv1Cwd/KpnkiKujb6LEfLULWbXioikKcO5fj3LkcK38rGkWw00n4TzgTx47lOH/IDHeJ4iCGvxr2bQNAA8H4DhCXilWUjTN7beh6wyLYrheB9n0IenoRaNcLO7FzRJ8KMaKC3hsMyiH/zSAQCOANRlezq4gVDGDlb8Ha/T2O3euxCkPTA8HkrqHpmaoSrJzvsPZsDnOhoj4KsCryoSIfIPRLOr4dxLdH1ZTj+iGztt+/tlwEU7rv/9ct9DWpS+iArQjYBTqiErXIH4TSMtymGQnrJipoHfoFWuSXoG8OqroUa18WZn4W1r6tWPu2hTbuKYOgpyf+fudDdQnW7vWYJbvDXa44DgqNVbEPKvbVXmdbMdgJ7SEmEVVTgXPnCtSWL2pv15abYFIn7MTOBBM7Yid0CP2Lb48dlwJGy0RwRAU97A97CSURSbRGVZdilu3BKNuDWbIbs/g/mEXZGNWhYyi0MrCTuxLodhraGYuZtwmzYDuqQPZ/j2ZGoBqj+D9Q/NN1WhnYsanY8e1QlhNqKrD2/oBj5/LQqRIPLIdCxyZjx3mw41KxY1PQMcnYsSnYMUlodyJ2TCLanQTm8UV1g+69bt06Zs+ejW3bjBo1itGjR9e53e/3M23aNLZv305CQgJ33XUXHTp0OK7ChGh22kb5qlA1lShvKUZ1Kaq6DKO6FKOy8Kd/FQWowE9dVLVhYSd1Cs3TuhPAV421LwujOBuzODuML0hEAqVtzMoCzMqCQ26zHW60OxEd50Fb7lD7ZV8lVuFO1O71KH/1YR9TW25sd0LoqN6hnzS6pnqD3rZtZs2axUMPPYTH4+GBBx4gPT2dbt261S7z+eefExcXxwsvvMDSpUv55z//yd37uzyKNkJrQIe2YHHge33QVuADl0MjGqXtn27Xdu0/ddD32Psv20HQwdBlOxC6bAdC3wcDoXlSe//XoA8VOPC1JvTPXwMBL8pfjfJV7/+6P+A5/FZq252IjklExyQRSO6Kttzgr8Io3YNRWYhZnINZnNMCK1ZEE8PvBb8XyvMPe7sGtDMe2xWLdsai3AloI7SHjwr6IHBsbdvrDfqsrCw6depEx44dARg2bBgrV66sE/SrVq3immuuAWDIkCG88soraK3r7cOS/NrYYyq69QnTLi+H7GpzlDqOtKw+6Ps613HEkIwEWhlgudCWM/TVdIS+OmPQMYnYphNMB9owUcFA6AeoqhjDV4XhLUd5yzC8ZeF+GaKNUYDyVWD4Kpr0cesN+qKiIjyen87K5PF42Lp16xGXMU2T2NhYysvLSUxMrLNcZmYmmZmh3ckmTpzIhS+uP+4XIIQQ4ujq3fHzcB0Sfj5Sb8gyABkZGUycOJGJEycyfvz4xtTZIqSmhovEuqSmhpGaGi4S6zqWmuoNeo/HQ2FhYe3lwsJCUlJSjrhMMBikqqqK+PjI7RUhhBBtSb1Bn5aWRl5eHvn5+QQCAZYtW0b6/va7BwwePJjFixcD8O233zJgwADpky6EEBHCfOyxxx472gKGYdCpUydeeOEFPv74Y4YPH86QIUOYO3cuXq+XLl260KNHD77++mvefPNNdu7cyR//+McGjeh79+7dVK+jyUhNDReJdUlNDSM1NVwk1tXYmsLaplgIIUTzi9wuPEIIIZqEBL0QQkS5sPS6qa+lQkuYMWMGa9asISkpicmTJwNQUVHBlClT2LdvH+3bt+fuu+9u0b2HCgoKmD59OiUlJSilyMjI4NJLLw1rXT6fj0cffZRAIEAwGGTIkCGMGTOG/Px8pk6dSkVFBb169eJPf/pTi3cdtW2b8ePHk5qayvjx4yOipttuuw23241hGJimycSJE8P+uaqsrGTmzJlkZ2ejlOLWW2+lS5cuYaspNzeXKVOm1F7Oz89nzJgxjBgxIqzracGCBXz++ecopejevTvjxo2jpKQkrJ+pjz76iM8++wytNaNGjeKyyy47ts+TbmHBYFDffvvtes+ePdrv9+t7771XZ2dnt3QZeuPGjXrbtm36nnvuqb3u9ddf1++//77WWuv3339fv/766y1aU1FRkd62bZvWWuuqqip9xx136Ozs7LDWZdu2rq6u1lpr7ff79QMPPKB//PFHPXnyZP31119rrbV+6aWX9CeffNJiNR0wf/58PXXqVP30009rrXVE1DRu3DhdWlpa57pwf65eeOEFnZmZqbUOvYcVFRVhr+mAYDCo//CHP+j8/Pyw1lRYWKjHjRuna2pqtNahz9IXX3wR1s/Url279D333KO9Xq8OBAL6iSee0Lm5uce0nlp86ubglgqWZdW2VGhp/fv3P+S34MqVKxkxYgQAI0aMaPG6UlJSaremx8TE0LVrV4qKisJal1IKt9sNhI6RCAaDKKXYuHEjQ4YMAeD8889v8XVVWFjImjVrGDVqFBA6aC/cNR1JON+/qqoqNm/ezAUXXACAZVnExcWF/bN+wPr16+nUqRPt27cPe022bePz+QgGg/h8PpKTk8P6mdq9ezd9+/bF5XJhmiYnn3wyK1asOKb11OJTNw1pqRAupaWltQeDpaSkUFYWvl4n+fn57Nixgz59+oS9Ltu2uf/++9mzZw8XX3wxHTt2JDY2FtMMnUczNTWVoqKiFq1pzpw53HjjjVRXh7r9lZeXh72mAyZMmADAhRdeSEZGRljfv/z8fBITE5kxYwa7du2id+/e3HTTTWH/TB2wdOlSzjnnHCC8P3+pqalcccUV3HrrrTidTk477TR69+4d1s9U9+7deeuttygvL8fpdLJ27VrS0tKOaT21eNDrBrZLaMu8Xi+TJ0/mpptuIjY2NtzlYBgGkyZNorKykueee47du8N7Ao3Vq1eTlJRE79692bhxY1hr+bknn3yS1NRUSktLeeqpp+jSpUtY6wkGg+zYsYOxY8fSt29fZs+ezbx588Ja0wGBQIDVq1dz/fXXh7sUKioqWLlyJdOnTyc2Npbnn3+edevWhbWmbt26ceWVV/LUU0/hdrs54YQTMI7xdIUtHvQNaakQLklJSRQXF5OSkkJxcfEhTdlaQiAQYPLkyQwfPpyzzz47YuoCiIuLo3///mzdupWqqiqCwSCmaVJUVERqamqL1fHjjz+yatUq1q5di8/no7q6mjlz5oS1pgMOPGdSUhJnnnkmWVlZYX3/PB4PHo+Hvn37AqHusvPmzYuIz9TatWvp1asXycnJQHg/5+vXr6dDhw61z3n22Wfz448/hv0zdcEFF9ROu7355pt4PJ5jWk8tPkffkJYK4ZKens6XX34JwJdffsmZZ57Zos+vtWbmzJl07dqVyy+/PCLqKisro7KyEgjtgbN+/Xq6du3KgAED+PbbbwFYvHhxi76H119/PTNnzmT69OncddddDBw4kDvuuCOsNUHoL7EDU0ler5fvv/+eHj16hPX9S05OxuPxkJubC4QCrVu3bmH/rEPdaRsI7+e8Xbt2bN26lZqaGrTWtesp3J+p0tJSILRH3ooVKzjnnHOOaT2F5cjYNWvW8Oqrr2LbNiNHjuSqq65q6RKYOnUqmzZtory8nKSkJMaMGcOZZ57JlClTKCgooF27dtxzzz0tunvXDz/8wCOPPEKPHj1qp7Ouu+46+vbtG7a6du3axfTp07FtG601Q4cO5de//jV79+49ZLczh8PRIjUdbOPGjcyfP5/x48eHvaa9e/fy3HPPAaEpk3PPPZerrrqK8vLysH6udu7cycyZMwkEAnTo0IFx48ahtQ5rTTU1Ndx6661Mmzatdnoy3Ovp7bffZtmyZZimSc+ePbnlllsoKioK62fqkUceoby8HMuy+O1vf8spp5xyTOtJWiAIIUSUkyNjhRAiyknQCyFElJOgF0KIKCdBL4QQUU6CXgghopwEvWjTpk+fzltvvRXuMoRoVhL0QggR5STohRAiyoXlxCNChMuOHTuYOXMmeXl5nH766bVHIFdUVDBt2jS2bt2KbduceOKJ3HzzzXg8Hr755hvmzZvHM888U/s48+fPZ/Pmzdx3333heilCNJiM6EWbEQgEmDRpEsOHD+eVV15h6NChLF++HAj1GTr//POZMWMGM2bMwOl0MmvWLCDUgyU/P5+cnJzax/rqq68477zzwvI6hGgsCXrRZmzZsoVgMMhll12GZVkMGTKEtLQ0ABISEhgyZAgul4uYmBiuuuoqNm/eDIDD4WDYsGF89dVXAGRnZ7Nv3z4GDx4cttciRGPI1I1oM4qLi0lNTa1z/oN27doBoSZbr776KuvWravt1lldXY1t2xiGwYgRI/jb3/7Gtddey5IlSxg6dGhYmrgJcSxkRC/ajJSUFIqKiuqc/ObAuRHmz59Pbm4uf/3rX3n11Vd5/PHHgZ9OlNOvXz8sy2Lz5s18/fXXMm0jWhUJetFm9OvXD8MwWLRoEcFgkOXLl5OVlQWE+sc7nU5iY2OpqKjgnXfeOeT+I0aM4JVXXsE0TU466aSWLl+IYyZBL9oMy7K49957Wbx4Mb/73e9YtmwZZ511FgCXXnopPp+P3//+9/zlL39h0KBBh9z/vPPOIzs7W0bzotWRfvRCNJDP5+MPf/gDzzzzDJ07dw53OUI0mIzohWigTz/9lLS0NAl50erIXjdCNMBtt92G1po///nP4S5FiEaTqRshhIhyMnUjhBBRToJeCCGinAS9EEJEOQl6IYSIchL0QggR5f4/1m79Ve0We/4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effective_contact_rate = 0.5\n",
    "recovery_rate = 1/4\n",
    "\n",
    "# We'll compute this for fun\n",
    "print(\"R0 is\", effective_contact_rate / recovery_rate)\n",
    "\n",
    "# What's our start population look like?\n",
    "# Everyone not infected or recovered is susceptible\n",
    "total_pop = 10086292\n",
    "recovered = 0\n",
    "infected = 1\n",
    "susceptible = total_pop - infected - recovered\n",
    "\n",
    "# A list of days, 0-91\n",
    "days = range(0, 91)\n",
    "\n",
    "# Use differential equations magic with our population\n",
    "ret = odeint(deriv,\n",
    "             [susceptible, infected, recovered],\n",
    "             days,\n",
    "             args=(total_pop, effective_contact_rate, recovery_rate))\n",
    "S, I, R = ret.T\n",
    "\n",
    "# Build a dataframe because why not\n",
    "df = pd.DataFrame({\n",
    "    'suseptible': S,\n",
    "    'infected': I,\n",
    "    'recovered': R,\n",
    "    'day': days\n",
    "})\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "df.plot(x='day',\n",
    "        y=['infected', 'suseptible', 'recovered'],\n",
    "        color=['#bb6424', '#aac6ca', '#cc8ac0'],\n",
    "        kind='area',\n",
    "        stacked=True)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}