06_District_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get('https://api.covid19india.org/state_district_wise.json')\n",
    "resp = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = resp.keys()\n",
    "districts = []\n",
    "for state in states:\n",
    "    ddata = resp[state][\"districtData\"]\n",
    "    for dist_name in ddata:\n",
    "        districts.append({\n",
    "            \"name\": dist_name,\n",
    "            \"active\": ddata[dist_name][\"active\"],\n",
    "            \"confirmed\": ddata[dist_name][\"confirmed\"],\n",
    "            \"deceased\": ddata[dist_name][\"deceased\"],\n",
    "            \"recovered\": ddata[dist_name][\"recovered\"],\n",
    "        })\n",
    "\n",
    "df = pd.DataFrame(districts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .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>active</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deceased</th>\n",
       "      <th>name</th>\n",
       "      <th>recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Imphal West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     active  confirmed  deceased         name  recovered\n",
       "261       0          1         0  Imphal West          1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56612"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_sum = df['confirmed'].sum()\n",
    "cnf_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>confirmed_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>11394</td>\n",
       "      <td>20.126475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>5192</td>\n",
       "      <td>9.171200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>4991</td>\n",
       "      <td>8.816152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2647</td>\n",
       "      <td>4.675687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Pune</td>\n",
       "      <td>2129</td>\n",
       "      <td>3.760687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Thane</td>\n",
       "      <td>1889</td>\n",
       "      <td>3.336748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Indore</td>\n",
       "      <td>1699</td>\n",
       "      <td>3.001131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Jaipur</td>\n",
       "      <td>1137</td>\n",
       "      <td>2.008408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Jodhpur</td>\n",
       "      <td>851</td>\n",
       "      <td>1.503215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Surat</td>\n",
       "      <td>799</td>\n",
       "      <td>1.411362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>783</td>\n",
       "      <td>1.383099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Agra</td>\n",
       "      <td>670</td>\n",
       "      <td>1.183495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>Bhopal</td>\n",
       "      <td>652</td>\n",
       "      <td>1.151699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>614</td>\n",
       "      <td>1.084576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kurnool</td>\n",
       "      <td>547</td>\n",
       "      <td>0.966226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Nashik</td>\n",
       "      <td>533</td>\n",
       "      <td>0.941497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Vadodara</td>\n",
       "      <td>440</td>\n",
       "      <td>0.777220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Aurangabad</td>\n",
       "      <td>400</td>\n",
       "      <td>0.706564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guntur</td>\n",
       "      <td>374</td>\n",
       "      <td>0.660637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Cuddalore</td>\n",
       "      <td>356</td>\n",
       "      <td>0.628842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  confirmed  confirmed_pct\n",
       "239      Mumbai      11394      20.126475\n",
       "87      Unknown       5192       9.171200\n",
       "92    Ahmedabad       4991       8.816152\n",
       "410     Chennai       2647       4.675687\n",
       "248        Pune       2129       3.760687\n",
       "255       Thane       1889       3.336748\n",
       "277      Indore       1699       3.001131\n",
       "358      Jaipur       1137       2.008408\n",
       "363     Jodhpur        851       1.503215\n",
       "119       Surat        799       1.411362\n",
       "527     Kolkata        783       1.383099\n",
       "448        Agra        670       1.183495\n",
       "268      Bhopal        652       1.151699\n",
       "379   Hyderabad        614       1.084576\n",
       "7       Kurnool        547       0.966226\n",
       "243      Nashik        533       0.941497\n",
       "122    Vadodara        440       0.777220\n",
       "227  Aurangabad        400       0.706564\n",
       "5        Guntur        374       0.660637\n",
       "412   Cuddalore        356       0.628842"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_df = df[['name', 'confirmed']][:]\n",
    "pct_df['confirmed_pct'] = 100 * pct_df['confirmed'] / cnf_sum\n",
    "pct_df.sort_values(by='confirmed_pct', ascending=False).head(20)"
   ]
  }
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