Italy_Data_Loader.ipynb

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    "# Italy Region/Province-Level Data Loader\n",
    "\n",
    "Data from: https://github.com/pcm-dpc/COVID-19"
   ]
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   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import cufflinks as cf\n",
    "cf.go_offline()"
   ]
  },
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   "execution_count": 2,
   "metadata": {},
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     "text": [
      "Fetching region data, from 02-24-2020 to 03-27-2020 please wait...\n",
      "Fetching province data, from 02-24-2020 to 03-27-2020 please wait...\n",
      "Finished fetching data!\n"
     ]
    }
   ],
   "source": [
    "italy_province_url_base = \"https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-province/dpc-covid19-ita-province-%s.csv\"\n",
    "italy_region_url_base = \"https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-%s.csv\"\n",
    "def gather_daily_italy_reports(date_rng, province=True):\n",
    "    collected = pd.DataFrame()\n",
    "    #Iterate over dates in provided range\n",
    "    print(\"Fetching %s data, from %s to %s please wait...\" % (\n",
    "        \"province\" if province else \"region\", \n",
    "        date_rng[0].strftime(\"%m-%d-%Y\"), \n",
    "        date_rng[-1].strftime(\"%m-%d-%Y\"))\n",
    "     )\n",
    "    for date in date_rng:\n",
    "        date_str = date.strftime(\"%Y%m%d\")\n",
    "        #print(\"Collecting data from: %s\" % date_str)\n",
    "        try:\n",
    "            #Try to fetch the day's .csv data from github\n",
    "            daily_report = pd.read_csv((italy_province_url_base if province else italy_region_url_base) % date_str)\n",
    "            #Insert a column for the date which this data was collected \n",
    "            daily_report.insert(0, \"date\", date)\n",
    "            #Append day's data to aggregated dataframe \n",
    "            collected = pd.concat([collected, daily_report], axis=0, ignore_index=True)\n",
    "        except:\n",
    "            print(\"FAILED TO FETCH DATA FOR: %s\" % date.strftime(\"%m-%d-%Y\"))\n",
    "    #Renaming columns\n",
    "    collected.rename(columns={\n",
    "        'denominazione_regione':'region',\n",
    "        'denominazione_provincia': 'province',\n",
    "        'ricoverati_con_sintomi': 'hospitalized with symptoms',\n",
    "        'intensive care\t': 'hospitalized in intensive care',\n",
    "        'totale_ospedalizzati': 'total hospitalized',\n",
    "        'isolamento_domiciliare': 'in home isolation',\n",
    "        'totale_attualmente_positivi': 'total currently positive',\n",
    "        'nuovi_attualmente_positivi': 'total newly positive',\n",
    "        'dimessi_guariti': 'recovered',\n",
    "        'deceduti': 'deaths',\n",
    "        'totale_casi': 'total positive cases',\n",
    "        'swabs': 'tests performed'\n",
    "    }, inplace=True)\n",
    "    return collected\n",
    "#Fetch region-level data\n",
    "raw_region_data = gather_daily_italy_reports(pd.date_range(\"02-24-2020\", \"03-27-2020\").to_pydatetime(), province=False)\n",
    "raw_region_data.to_csv(\"output/italy_region_report_data.csv\", index=0)\n",
    "\n",
    "#Fetch province-level data\n",
    "raw_province_data = gather_daily_italy_reports(pd.date_range(\"02-24-2020\", \"03-27-2020\").to_pydatetime(), province=True)\n",
    "raw_province_data.to_csv(\"output/italy_province_report_data.csv\", index=0)\n",
    "print(\"Finished fetching data!\")"
   ]
  },
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    "## Province-Level Data"
   ]
  },
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   "execution_count": 3,
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    "clean_province_data = pd.read_csv(\"output/italy_province_report_data.csv\")\n",
    "province_name = \"Milano\"\n",
    "selected_province_data = clean_province_data[clean_province_data[\"province\"] == province_name]\n",
    "selected_province_data[\"total positive cases\"].iplot(title=\"Outbreak in Province: \" + province_name, xTitle=\"Date\", yTitle=\"Confirmed Cases\")"
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  {
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     "text": [
      "province\n",
      "Bergamo                  8060.0\n",
      "Milano                   7469.0\n",
      "Brescia                  7305.0\n",
      "Cremona                  3496.0\n",
      "Torino                   3361.0\n",
      "Piacenza                 2276.0\n",
      "Lodi                     2006.0\n",
      "Monza e della Brianza    1948.0\n",
      "Padova                   1891.0\n",
      "Reggio nell'Emilia       1861.0\n",
      "Modena                   1772.0\n",
      "Pavia                    1712.0\n",
      "Roma                     1703.0\n",
      "Parma                    1690.0\n",
      "Verona                   1645.0\n",
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\"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Outbreak in Worst Hit Provinces of Italy\"}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Date\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Confirmed Cases\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4acda904-663f-48ca-a7be-eab22c00bb7e');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_provinces = clean_province_data.groupby(\"province\")[\"total positive cases\"].max().sort_values(ascending=False).head(15)\n",
    "print(top_provinces)\n",
    "top_province_data = clean_province_data[clean_province_data[\"province\"].isin(top_provinces.keys())]\n",
    "province_plot_data = top_province_data.pivot(index=\"date\", columns=\"province\", values=\"total positive cases\")\n",
    "province_plot_data.iplot(title=\"Outbreak in Worst Hit Provinces of Italy\", xTitle=\"Date\", yTitle=\"Confirmed Cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Region-Level Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>region</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>hospitalized with symptoms</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>total hospitalized</th>\n",
       "      <th>in home isolation</th>\n",
       "      <th>total currently positive</th>\n",
       "      <th>total newly positive</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>total positive cases</th>\n",
       "      <th>tamponi</th>\n",
       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>42.351222</td>\n",
       "      <td>13.398438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>17</td>\n",
       "      <td>Basilicata</td>\n",
       "      <td>40.639471</td>\n",
       "      <td>15.805148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>4</td>\n",
       "      <td>P.A. Bolzano</td>\n",
       "      <td>46.499335</td>\n",
       "      <td>11.356624</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>18</td>\n",
       "      <td>Calabria</td>\n",
       "      <td>38.905976</td>\n",
       "      <td>16.594402</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>15</td>\n",
       "      <td>Campania</td>\n",
       "      <td>40.839566</td>\n",
       "      <td>14.250850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-03-27T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>9</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>43.769231</td>\n",
       "      <td>11.255889</td>\n",
       "      <td>1053</td>\n",
       "      <td>274</td>\n",
       "      <td>1327</td>\n",
       "      <td>1843</td>\n",
       "      <td>3170</td>\n",
       "      <td>197</td>\n",
       "      <td>103</td>\n",
       "      <td>177</td>\n",
       "      <td>3450</td>\n",
       "      <td>23746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-03-27T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>4</td>\n",
       "      <td>P.A. Trento</td>\n",
       "      <td>46.068935</td>\n",
       "      <td>11.121231</td>\n",
       "      <td>341</td>\n",
       "      <td>66</td>\n",
       "      <td>407</td>\n",
       "      <td>757</td>\n",
       "      <td>1164</td>\n",
       "      <td>70</td>\n",
       "      <td>125</td>\n",
       "      <td>102</td>\n",
       "      <td>1391</td>\n",
       "      <td>4923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-03-27T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>10</td>\n",
       "      <td>Umbria</td>\n",
       "      <td>43.106758</td>\n",
       "      <td>12.388247</td>\n",
       "      <td>149</td>\n",
       "      <td>46</td>\n",
       "      <td>195</td>\n",
       "      <td>629</td>\n",
       "      <td>824</td>\n",
       "      <td>54</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>884</td>\n",
       "      <td>6137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-03-27T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2</td>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>45.737503</td>\n",
       "      <td>7.320149</td>\n",
       "      <td>91</td>\n",
       "      <td>24</td>\n",
       "      <td>115</td>\n",
       "      <td>298</td>\n",
       "      <td>413</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>452</td>\n",
       "      <td>1252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-03-27T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>5</td>\n",
       "      <td>Veneto</td>\n",
       "      <td>45.434905</td>\n",
       "      <td>12.338452</td>\n",
       "      <td>1536</td>\n",
       "      <td>338</td>\n",
       "      <td>1874</td>\n",
       "      <td>4774</td>\n",
       "      <td>6648</td>\n",
       "      <td>508</td>\n",
       "      <td>536</td>\n",
       "      <td>313</td>\n",
       "      <td>7497</td>\n",
       "      <td>83627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>693 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date                 data stato  codice_regione         region  \\\n",
       "0    2020-02-24  2020-02-24T18:00:00   ITA              13        Abruzzo   \n",
       "1    2020-02-24  2020-02-24T18:00:00   ITA              17     Basilicata   \n",
       "2    2020-02-24  2020-02-24T18:00:00   ITA               4   P.A. Bolzano   \n",
       "3    2020-02-24  2020-02-24T18:00:00   ITA              18       Calabria   \n",
       "4    2020-02-24  2020-02-24T18:00:00   ITA              15       Campania   \n",
       "..          ...                  ...   ...             ...            ...   \n",
       "688  2020-03-27  2020-03-27T17:00:00   ITA               9        Toscana   \n",
       "689  2020-03-27  2020-03-27T17:00:00   ITA               4    P.A. Trento   \n",
       "690  2020-03-27  2020-03-27T17:00:00   ITA              10         Umbria   \n",
       "691  2020-03-27  2020-03-27T17:00:00   ITA               2  Valle d'Aosta   \n",
       "692  2020-03-27  2020-03-27T17:00:00   ITA               5         Veneto   \n",
       "\n",
       "           lat       long  hospitalized with symptoms  terapia_intensiva  \\\n",
       "0    42.351222  13.398438                           0                  0   \n",
       "1    40.639471  15.805148                           0                  0   \n",
       "2    46.499335  11.356624                           0                  0   \n",
       "3    38.905976  16.594402                           0                  0   \n",
       "4    40.839566  14.250850                           0                  0   \n",
       "..         ...        ...                         ...                ...   \n",
       "688  43.769231  11.255889                        1053                274   \n",
       "689  46.068935  11.121231                         341                 66   \n",
       "690  43.106758  12.388247                         149                 46   \n",
       "691  45.737503   7.320149                          91                 24   \n",
       "692  45.434905  12.338452                        1536                338   \n",
       "\n",
       "     total hospitalized  in home isolation  total currently positive  \\\n",
       "0                     0                  0                         0   \n",
       "1                     0                  0                         0   \n",
       "2                     0                  0                         0   \n",
       "3                     0                  0                         0   \n",
       "4                     0                  0                         0   \n",
       "..                  ...                ...                       ...   \n",
       "688                1327               1843                      3170   \n",
       "689                 407                757                      1164   \n",
       "690                 195                629                       824   \n",
       "691                 115                298                       413   \n",
       "692                1874               4774                      6648   \n",
       "\n",
       "     total newly positive  recovered  deaths  total positive cases  tamponi  \\\n",
       "0                       0          0       0                     0        5   \n",
       "1                       0          0       0                     0        0   \n",
       "2                       0          0       0                     0        1   \n",
       "3                       0          0       0                     0        1   \n",
       "4                       0          0       0                     0       10   \n",
       "..                    ...        ...     ...                   ...      ...   \n",
       "688                   197        103     177                  3450    23746   \n",
       "689                    70        125     102                  1391     4923   \n",
       "690                    54         39      21                   884     6137   \n",
       "691                    35          2      37                   452     1252   \n",
       "692                   508        536     313                  7497    83627   \n",
       "\n",
       "    note_it note_en  \n",
       "0       NaN     NaN  \n",
       "1       NaN     NaN  \n",
       "2       NaN     NaN  \n",
       "3       NaN     NaN  \n",
       "4       NaN     NaN  \n",
       "..      ...     ...  \n",
       "688     NaN     NaN  \n",
       "689     NaN     NaN  \n",
       "690     NaN     NaN  \n",
       "691     NaN     NaN  \n",
       "692     NaN     NaN  \n",
       "\n",
       "[693 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_region_data = pd.read_csv(\"output/italy_region_report_data.csv\")\n",
    "clean_region_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region\n",
      "Lombardia         37298\n",
      "Emilia Romagna    11588\n",
      "Veneto             7497\n",
      "Piemonte           7092\n",
      "Toscana            3450\n",
      "Marche             3196\n",
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   ],
   "source": [
    "top_regions = clean_region_data.groupby(\"region\")[\"total positive cases\"].max().sort_values(ascending=False).head(10)\n",
    "print(top_regions)\n",
    "top_region_data = clean_region_data[clean_region_data[\"region\"].isin(top_regions.keys())]\n",
    "region_plot_data = top_region_data.pivot(index=\"date\", columns=\"region\", values=\"total positive cases\")\n",
    "region_plot_data.iplot(title=\"Outbreak in Worst Hit Regions of Italy\", xTitle=\"Date\", yTitle=\"Confirmed Cases\")"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region\n",
      "Lombardia         5402\n",
      "Emilia Romagna    1267\n",
      "Piemonte           569\n",
      "Marche             336\n",
      "Liguria            331\n",
      "Veneto             313\n",
      "Toscana            177\n",
      "Lazio              118\n",
      "P.A. Trento        102\n",
      "Campania            98\n",
      "Name: deaths, dtype: int64\n"
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   ],
   "source": [
    "top_regions = clean_region_data.groupby(\"region\")[\"deaths\"].max().sort_values(ascending=False).head(10)\n",
    "print(top_regions)\n",
    "top_region_data = clean_region_data[clean_region_data[\"region\"].isin(top_regions.keys())]\n",
    "region_plot_data = top_region_data.pivot(index=\"date\", columns=\"region\", values=\"deaths\")\n",
    "region_plot_data.iplot(title=\"Outbreak in Worst Hit Regions of Italy\", xTitle=\"Date\", yTitle=\"Deaths\")"
   ]
  }
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