refactoring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports/03-29-2020.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "def get_daily_csv(url: str) -> pd.DataFrame:\n",
    "    header = {\n",
    "      \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36\",\n",
    "      \"X-Requested-With\": \"XMLHttpRequest\"\n",
    "    }\n",
    "    r = requests.get(url, headers=header).text\n",
    "    return pd.read_html(r, header=[0], index_col=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def covid19_repo() -> str:\n",
    "    return 'https://github.com/CSSEGISandData/COVID-19'\n",
    "\n",
    "\n",
    "def daily_csv(date: str) -> str:\n",
    "    return '/'.join([covid19_repo(), f'tree/master/csse_covid_19_data/csse_covid_19_daily_reports/{date}.csv'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_daily_csv(daily_csv('03-29-2020'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Lat</th>\n",
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       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "      <td>3434.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>36.846297</td>\n",
       "      <td>-82.742830</td>\n",
       "      <td>209.702097</td>\n",
       "      <td>9.879150</td>\n",
       "      <td>43.413512</td>\n",
       "      <td>115.202679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.097413</td>\n",
       "      <td>39.184680</td>\n",
       "      <td>2956.173537</td>\n",
       "      <td>234.856826</td>\n",
       "      <td>1161.782648</td>\n",
       "      <td>2024.521259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-42.882100</td>\n",
       "      <td>-170.132000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.953228</td>\n",
       "      <td>-97.628666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.067870</td>\n",
       "      <td>-89.041582</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41.728181</td>\n",
       "      <td>-81.716167</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>71.706900</td>\n",
       "      <td>178.065000</td>\n",
       "      <td>97689.000000</td>\n",
       "      <td>10779.000000</td>\n",
       "      <td>62570.000000</td>\n",
       "      <td>73880.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          Lat        Long_     Confirmed        Deaths  \\\n",
       "count         0.0  3434.000000  3434.000000   3434.000000   3434.000000   \n",
       "mean          NaN    36.846297   -82.742830    209.702097      9.879150   \n",
       "std           NaN    10.097413    39.184680   2956.173537    234.856826   \n",
       "min           NaN   -42.882100  -170.132000      0.000000      0.000000   \n",
       "25%           NaN    33.953228   -97.628666      0.000000      0.000000   \n",
       "50%           NaN    38.067870   -89.041582      2.000000      0.000000   \n",
       "75%           NaN    41.728181   -81.716167     10.000000      0.000000   \n",
       "max           NaN    71.706900   178.065000  97689.000000  10779.000000   \n",
       "\n",
       "          Recovered        Active  \n",
       "count   3434.000000   3434.000000  \n",
       "mean      43.413512    115.202679  \n",
       "std     1161.782648   2024.521259  \n",
       "min        0.000000     -6.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max    62570.000000  73880.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
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       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45001.0</th>\n",
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       "      <td>South Carolina</td>\n",
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       "      <td>2020-03-29 23:08:25</td>\n",
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       "      <td>2020-03-29 23:08:25</td>\n",
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       "      <td>Acadia, Louisiana, US</td>\n",
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       "      <th>51001.0</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>19001.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Adair</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-29 23:08:25</td>\n",
       "      <td>41.330756</td>\n",
       "      <td>-94.471059</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Adair, Iowa, US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0     Admin2  Province_State Country_Region  \\\n",
       "FIPS                                                            \n",
       "45001.0         NaN  Abbeville  South Carolina             US   \n",
       "22001.0         NaN     Acadia       Louisiana             US   \n",
       "51001.0         NaN   Accomack        Virginia             US   \n",
       "16001.0         NaN        Ada           Idaho             US   \n",
       "19001.0         NaN      Adair            Iowa             US   \n",
       "\n",
       "                 Last_Update        Lat       Long_  Confirmed  Deaths  \\\n",
       "FIPS                                                                     \n",
       "45001.0  2020-03-29 23:08:25  34.223334  -82.461707          3       0   \n",
       "22001.0  2020-03-29 23:08:25  30.295065  -92.414197          9       1   \n",
       "51001.0  2020-03-29 23:08:25  37.767072  -75.632346          3       0   \n",
       "16001.0  2020-03-29 23:08:25  43.452658 -116.241552         92       1   \n",
       "19001.0  2020-03-29 23:08:25  41.330756  -94.471059          1       0   \n",
       "\n",
       "         Recovered  Active                   Combined_Key  \n",
       "FIPS                                                       \n",
       "45001.0          0       0  Abbeville, South Carolina, US  \n",
       "22001.0          0       0          Acadia, Louisiana, US  \n",
       "51001.0          0       0         Accomack, Virginia, US  \n",
       "16001.0          0       0                 Ada, Idaho, US  \n",
       "19001.0          0       0                Adair, Iowa, US  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
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       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Combined_Key</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS</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",
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       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2020-03-29 23:08:13</td>\n",
       "      <td>6.423800</td>\n",
       "      <td>-66.589700</td>\n",
       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>78</td>\n",
       "      <td>Venezuela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2020-03-29 23:08:13</td>\n",
       "      <td>14.058324</td>\n",
       "      <td>108.277199</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>163</td>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>2020-03-29 23:08:13</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>90</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020-03-29 23:08:13</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-03-29 23:08:13</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 Admin2 Province_State      Country_Region  \\\n",
       "FIPS                                                         \n",
       "NaN          NaN    NaN            NaN           Venezuela   \n",
       "NaN          NaN    NaN            NaN             Vietnam   \n",
       "NaN          NaN    NaN            NaN  West Bank and Gaza   \n",
       "NaN          NaN    NaN            NaN              Zambia   \n",
       "NaN          NaN    NaN            NaN            Zimbabwe   \n",
       "\n",
       "              Last_Update        Lat       Long_  Confirmed  Deaths  \\\n",
       "FIPS                                                                  \n",
       "NaN   2020-03-29 23:08:13   6.423800  -66.589700        119       2   \n",
       "NaN   2020-03-29 23:08:13  14.058324  108.277199        188       0   \n",
       "NaN   2020-03-29 23:08:13  31.952200   35.233200        109       1   \n",
       "NaN   2020-03-29 23:08:13 -13.133897   27.849332         29       0   \n",
       "NaN   2020-03-29 23:08:13 -19.015438   29.154857          7       1   \n",
       "\n",
       "      Recovered  Active        Combined_Key  \n",
       "FIPS                                         \n",
       "NaN          39      78           Venezuela  \n",
       "NaN          25     163             Vietnam  \n",
       "NaN          18      90  West Bank and Gaza  \n",
       "NaN           0      29              Zambia  \n",
       "NaN           0       6            Zimbabwe  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_daily_csv():\n",
    "    assert url == daily_csv('03-29-2020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_daily_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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