CovIndia_API_basic_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using CovIndia API \n",
    "\n",
    "In this notebook, we will show\n",
    "- How to use the CovIndia API, to generate CSVs (which can be opened in Microsoft Excel)\n",
    "- Do some basic analysis like day-wise state and district numbers.\n",
    "- A small example of plotting the infected numbers for a given state, and doing some curve fitting to smoothen the graph. \n",
    "- An example of the kind of analysis possible with our data.\n",
    "\n",
    "We hope the data science and research community will take this analysis further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using CovIndia API to generate CSV containing the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests        # Needs to be installed (pip install requests)\n",
    "import json\n",
    "import pandas as pd    # Needs to be installed (pip install pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://v1.api.covindia.com/covindia-raw-data'\n",
    "raw_data = requests.get(url=url).json()\n",
    "# Next line is just to change the type of keys from string to int\n",
    "# This is required for making the dataframe automatically\n",
    "raw_data = {int(old_key): val for old_key, val in raw_data.items()}\n",
    "#print(raw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>district</th>\n",
       "      <th>state</th>\n",
       "      <th>infected</th>\n",
       "      <th>death</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>22:17</td>\n",
       "      <td>Rangareddi</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>22:17</td>\n",
       "      <td>Khammam</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>22:17</td>\n",
       "      <td>Nalgonda</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>22:17</td>\n",
       "      <td>Nizamabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>22:17</td>\n",
       "      <td>Adilabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   time    district      state  infected  death  \\\n",
       "0 2020-04-07  22:17  Rangareddi  Telangana         5      0   \n",
       "1 2020-04-07  22:17     Khammam  Telangana         3      0   \n",
       "2 2020-04-07  22:17    Nalgonda  Telangana        21      0   \n",
       "3 2020-04-07  22:17   Nizamabad  Telangana         7      0   \n",
       "4 2020-04-07  22:17    Adilabad  Telangana        11      0   \n",
       "\n",
       "                                            source  \n",
       "0  https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg  \n",
       "1  https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg  \n",
       "2  https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg  \n",
       "3  https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg  \n",
       "4  https://pbs.twimg.com/media/EVAyEo6UYAAn0Iz.jpg  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df = pd.DataFrame.from_dict(raw_data, orient='index')\n",
    "raw_data_df  = raw_data_df [['date','time','district','state','infected','death','source']]\n",
    "raw_data_df[\"date\"] = pd.to_datetime(raw_data_df[\"date\"],dayfirst=True)\n",
    "raw_data_df.to_csv('raw_data.csv', index = False)\n",
    "raw_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performing some basic aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Infected : 5152\n",
      "Total Death : 168\n"
     ]
    }
   ],
   "source": [
    "# Finding the total infected and death\n",
    "print('Total Infected : ' + str(raw_data_df['infected'].sum()))\n",
    "print('Total Death : ' + str(raw_data_df['death'].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>district</th>\n",
       "      <th>infected</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kannur</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        date   district  infected  death\n",
       "0 2020-02-03     Kannur         1      0\n",
       "1 2020-03-02      Delhi         7      0\n",
       "2 2020-03-02  Hyderabad         5      0\n",
       "3 2020-03-04    Gurgaon        14      0\n",
       "4 2020-03-04     Jaipur         1      0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregating the data daywise and districtwise\n",
    "dist_day_df = raw_data_df.groupby(['date', 'district'], as_index=False)['infected','death'].sum()\n",
    "dist_day_df = dist_day_df.sort_values('date', ascending = True)\n",
    "dist_day_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>infected</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        date      state  infected  death\n",
       "0 2020-02-03     Kerala         1      0\n",
       "1 2020-03-02      Delhi         7      0\n",
       "2 2020-03-02  Telangana         5      0\n",
       "3 2020-03-04    Haryana        14      0\n",
       "4 2020-03-04  Rajasthan         1      0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregating the data daywise and statewise\n",
    "state_day_df = raw_data_df.groupby(['date', 'state'], as_index=False)['infected','death'].sum()\n",
    "state_day_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the data for a given state or district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Infected numbers for Maharashtra')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting infected numbers for a particular state\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "maharashtra_df = state_day_df[state_day_df['state']=='Maharashtra']\n",
    "#maharashtra_df  = maharashtra_df.sort_values('date', ascending=True)\n",
    "plt.plot(maharashtra_df['date'], maharashtra_df['infected'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title('Infected numbers for Maharashtra')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the graph is not very smooth. This is simply because of the testing as more tests are happening every alternate day. This has been observed at the India level. We can smoothen out these fluctuations to get a slightly better picture in terms of data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "import numpy as np\n",
    "\n",
    "ysmoothed = gaussian_filter1d(np.array(maharashtra_df['infected']), sigma=1.5)\n",
    "plt.plot(np.array(maharashtra_df['date']), ysmoothed, color='orange')\n",
    "plt.plot(maharashtra_df['date'], maharashtra_df['infected'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Further Analysis\n",
    "\n",
    "The most promising analysis would be based on weather as this data has district level accuracy. We did some preliminary analysis and found the following interesting, but as yet unconclusive results:\n",
    "\n",
    "<img src=\"delhi_trend.JPG\">\n",
    "\n",
    "Here we see a slight negative trend in the percentage increase in new cases as temperature increases. To be sure, each point here corresponds to a particular day in delhi, and the temperature is taken to be the temperature 5 days earlier as Covid-19 requires atleast that much time to be detected. We know that actually it could be anywhere between 5 - 14 days, but taking an average over such a large period will hide any effects due to temperature, as tll the averages will be virtually the same.\n",
    "\n",
    "This reconfirms the intuition that heat is bad for the spread of Virus and gives some hope in the case of countries like India. However, evn then, the effect of temperature is not as big as previously expected. If one naively extrapolates this trend, the percentage increase goes to zero only at 65 degrees centigrade. We will die of heat before that! \n",
    "\n",
    "You might say, we are cherry-picking and giving people false hope. While we agree that this is barely enough data to throw caution to the wind, interestingly even Mumbai data shows a similar picture.\n",
    "\n",
    "<img src=\"mumbai_trend.JPG\">\n",
    "\n",
    "One could look at similar trends when it comes to humidity or Atmospheric pressure. Population density could also play a key role in determining the speed of the spread. An analysis using the world cities all on one graph might give us enough data points to have a more conclusive result.\n",
    "\n",
    "We hope that this notebook will serve you as a base for further exploration. If you do find interesting results, *please share them with us*. Also, we would love if you cite our work in case you publish research articles or reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}