101 Pulling COVID-19 Data via the Statworx API to a DataFrame.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step #1 Define the Payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the data is provided from the european centre for disease prevention and control\n",
    "from datetime import date\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# define the payload that will be sent to the api endpoint, and the endpoint url\n",
    "# code defines the countries for which we will retrieve data\n",
    "# to retrieve data for specific countries use e.g. {'country': 'Germany'}\n",
    "payload = {\"code\": \"ALL\"}  \n",
    "URL = \"https://api.statworx.com/covid\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step #2 Call the REST API Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# call the api\n",
    "response = requests.post(url=URL, data=json.dumps(payload))\n",
    "response # if the request was successful, you should see a response code 200"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step #3 Convert the Data to a 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>date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>01</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>02</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>03</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>04</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61895</th>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11007</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61896</th>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11081</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61897</th>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11162</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61898</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11219</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61899</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>14645473.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11246</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61900 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date day month  year  cases  deaths      country code  \\\n",
       "0      2019-12-31  31    12  2019      0       0  Afghanistan   AF   \n",
       "1      2020-01-01  01    01  2020      0       0  Afghanistan   AF   \n",
       "2      2020-01-02  02    01  2020      0       0  Afghanistan   AF   \n",
       "3      2020-01-03  03    01  2020      0       0  Afghanistan   AF   \n",
       "4      2020-01-04  04    01  2020      0       0  Afghanistan   AF   \n",
       "...           ...  ..   ...   ...    ...     ...          ...  ...   \n",
       "61895  2020-12-10  10    12  2020     95       1     Zimbabwe   ZW   \n",
       "61896  2020-12-11  11    12  2020     74       1     Zimbabwe   ZW   \n",
       "61897  2020-12-12  12    12  2020     81       1     Zimbabwe   ZW   \n",
       "61898  2020-12-13  13    12  2020     57       1     Zimbabwe   ZW   \n",
       "61899  2020-12-14  14    12  2020     27       0     Zimbabwe   ZW   \n",
       "\n",
       "       population continent  cases_cum  deaths_cum  \n",
       "0      38041757.0      Asia          0           0  \n",
       "1      38041757.0      Asia          0           0  \n",
       "2      38041757.0      Asia          0           0  \n",
       "3      38041757.0      Asia          0           0  \n",
       "4      38041757.0      Asia          0           0  \n",
       "...           ...       ...        ...         ...  \n",
       "61895  14645473.0    Africa      11007         304  \n",
       "61896  14645473.0    Africa      11081         305  \n",
       "61897  14645473.0    Africa      11162         306  \n",
       "61898  14645473.0    Africa      11219         307  \n",
       "61899  14645473.0    Africa      11246         307  \n",
       "\n",
       "[61900 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the response data to a data frame\n",
    "df = pd.DataFrame.from_dict(json.loads(response.text))\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step #4 Filter the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>deaths_cum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10332</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>16</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>37411038.0</td>\n",
       "      <td>America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10333</th>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>17</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>37411038.0</td>\n",
       "      <td>America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334</th>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>18</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>37411038.0</td>\n",
       "      <td>America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10335</th>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>19</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>37411038.0</td>\n",
       "      <td>America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10336</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>20</td>\n",
       "      <td>01</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>37411038.0</td>\n",
       "      <td>America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date day month  year  cases  deaths country code  population  \\\n",
       "10332 2020-01-16  16    01  2020      0       0  Canada   CA  37411038.0   \n",
       "10333 2020-01-17  17    01  2020      0       0  Canada   CA  37411038.0   \n",
       "10334 2020-01-18  18    01  2020      0       0  Canada   CA  37411038.0   \n",
       "10335 2020-01-19  19    01  2020      0       0  Canada   CA  37411038.0   \n",
       "10336 2020-01-20  20    01  2020      0       0  Canada   CA  37411038.0   \n",
       "\n",
       "      continent  cases_cum  deaths_cum  \n",
       "10332   America          0           0  \n",
       "10333   America          0           0  \n",
       "10334   America          0           0  \n",
       "10335   America          0           0  \n",
       "10336   America          0           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert date column to date format\n",
    "df.loc[:, \"date\"] = pd.to_datetime(df[\"date\"], format=\"%Y-%m-%d\")\n",
    "\n",
    "# filter specific countries\n",
    "list_of_countries = [\"Germany\", \"Switzerland\", \"France\", \"Spain\", \"Canada\"]\n",
    "df_1 = df[df[\"country\"].isin(list_of_countries)]\n",
    "\n",
    "# filter the data to a specific timeframe\n",
    "df_new = df_1[df_1[\"date\"] > \"2020-01-15\"]\n",
    "df_new.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step #5 Plot the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create separate plot lines for each country in the dataset\n",
    "fig, ax1 = plt.subplots(figsize=(12, 8))\n",
    "plt.ylabel(\"Total Cases\", fontsize=20, color=\"black\")\n",
    "for countryname in list_of_countries:\n",
    "    x = df_new[df_new[\"country\"] == countryname][\"date\"]\n",
    "    y = df_new[df_new[\"country\"] == countryname][\"cases_cum\"]\n",
    "    plt.plot(x, y, label=countryname)\n",
    "\n",
    "plt.legend(list_of_countries, loc=\"upper left\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tfgpu",
   "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": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "80915ee0585988373cc19dbfd646c27e878a24ec37d0f08b6f8b5b6f154a2926"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}