covid19_italy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COVID-19\n",
    "### Italy, data source:  https://github.com/pcm-dpc/COVID-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### gathering data from https://github.com/pcm-dpc/COVID-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>ricoverati_con_sintomi</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>totale_ospedalizzati</th>\n",
       "      <th>isolamento_domiciliare</th>\n",
       "      <th>totale_positivi</th>\n",
       "      <th>variazione_totale_positivi</th>\n",
       "      <th>nuovi_positivi</th>\n",
       "      <th>dimessi_guariti</th>\n",
       "      <th>deceduti</th>\n",
       "      <th>totale_casi</th>\n",
       "      <th>tamponi</th>\n",
       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020-03-30T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>27795</td>\n",
       "      <td>3981</td>\n",
       "      <td>31776</td>\n",
       "      <td>43752</td>\n",
       "      <td>75528</td>\n",
       "      <td>1648</td>\n",
       "      <td>4050</td>\n",
       "      <td>14620</td>\n",
       "      <td>11591</td>\n",
       "      <td>101739</td>\n",
       "      <td>477359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-03-31T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>28192</td>\n",
       "      <td>4023</td>\n",
       "      <td>32215</td>\n",
       "      <td>45420</td>\n",
       "      <td>77635</td>\n",
       "      <td>2107</td>\n",
       "      <td>4053</td>\n",
       "      <td>15729</td>\n",
       "      <td>12428</td>\n",
       "      <td>105792</td>\n",
       "      <td>506968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-04-01T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>28403</td>\n",
       "      <td>4035</td>\n",
       "      <td>32438</td>\n",
       "      <td>48134</td>\n",
       "      <td>80572</td>\n",
       "      <td>2937</td>\n",
       "      <td>4782</td>\n",
       "      <td>16847</td>\n",
       "      <td>13155</td>\n",
       "      <td>110574</td>\n",
       "      <td>541423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                   data stato  ricoverati_con_sintomi  terapia_intensiva  \\\n",
       "35  2020-03-30T17:00:00   ITA                   27795               3981   \n",
       "36  2020-03-31T17:00:00   ITA                   28192               4023   \n",
       "37  2020-04-01T17:00:00   ITA                   28403               4035   \n",
       "\n",
       "    totale_ospedalizzati  isolamento_domiciliare  totale_positivi  \\\n",
       "35                 31776                   43752            75528   \n",
       "36                 32215                   45420            77635   \n",
       "37                 32438                   48134            80572   \n",
       "\n",
       "    variazione_totale_positivi  nuovi_positivi  dimessi_guariti  deceduti  \\\n",
       "35                        1648            4050            14620     11591   \n",
       "36                        2107            4053            15729     12428   \n",
       "37                        2937            4782            16847     13155   \n",
       "\n",
       "    totale_casi  tamponi note_it note_en  \n",
       "35       101739   477359     NaN     NaN  \n",
       "36       105792   506968     NaN     NaN  \n",
       "37       110574   541423     NaN     NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_italy = pd.read_csv('https://github.com/pcm-dpc/COVID-19/raw/master/dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv')\n",
    "df_italy.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>hospitalized</th>\n",
       "      <th>ICU</th>\n",
       "      <th>hospitalized_total</th>\n",
       "      <th>home_confinement</th>\n",
       "      <th>confirmed_active</th>\n",
       "      <th>variazione_totale_positivi</th>\n",
       "      <th>confirmed_new</th>\n",
       "      <th>recovered</th>\n",
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       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
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       "    <tr>\n",
       "      <th>date</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>101</td>\n",
       "      <td>26</td>\n",
       "      <td>127</td>\n",
       "      <td>94</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>229</td>\n",
       "      <td>4324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-25 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>114</td>\n",
       "      <td>35</td>\n",
       "      <td>150</td>\n",
       "      <td>162</td>\n",
       "      <td>311</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>322</td>\n",
       "      <td>8623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>128</td>\n",
       "      <td>36</td>\n",
       "      <td>164</td>\n",
       "      <td>221</td>\n",
       "      <td>385</td>\n",
       "      <td>74</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>400</td>\n",
       "      <td>9587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>248</td>\n",
       "      <td>56</td>\n",
       "      <td>304</td>\n",
       "      <td>284</td>\n",
       "      <td>588</td>\n",
       "      <td>203</td>\n",
       "      <td>250</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>650</td>\n",
       "      <td>12014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>345</td>\n",
       "      <td>64</td>\n",
       "      <td>409</td>\n",
       "      <td>412</td>\n",
       "      <td>821</td>\n",
       "      <td>233</td>\n",
       "      <td>238</td>\n",
       "      <td>46</td>\n",
       "      <td>21</td>\n",
       "      <td>888</td>\n",
       "      <td>15695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>401</td>\n",
       "      <td>105</td>\n",
       "      <td>506</td>\n",
       "      <td>543</td>\n",
       "      <td>1049</td>\n",
       "      <td>228</td>\n",
       "      <td>240</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>1128</td>\n",
       "      <td>18661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>639</td>\n",
       "      <td>140</td>\n",
       "      <td>779</td>\n",
       "      <td>798</td>\n",
       "      <td>1577</td>\n",
       "      <td>528</td>\n",
       "      <td>566</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>1694</td>\n",
       "      <td>21127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>742</td>\n",
       "      <td>166</td>\n",
       "      <td>908</td>\n",
       "      <td>927</td>\n",
       "      <td>1835</td>\n",
       "      <td>258</td>\n",
       "      <td>342</td>\n",
       "      <td>149</td>\n",
       "      <td>52</td>\n",
       "      <td>2036</td>\n",
       "      <td>23345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>1034</td>\n",
       "      <td>229</td>\n",
       "      <td>1263</td>\n",
       "      <td>1000</td>\n",
       "      <td>2263</td>\n",
       "      <td>428</td>\n",
       "      <td>466</td>\n",
       "      <td>160</td>\n",
       "      <td>79</td>\n",
       "      <td>2502</td>\n",
       "      <td>25856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>1346</td>\n",
       "      <td>295</td>\n",
       "      <td>1641</td>\n",
       "      <td>1065</td>\n",
       "      <td>2706</td>\n",
       "      <td>443</td>\n",
       "      <td>587</td>\n",
       "      <td>276</td>\n",
       "      <td>107</td>\n",
       "      <td>3089</td>\n",
       "      <td>29837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>1790</td>\n",
       "      <td>351</td>\n",
       "      <td>2141</td>\n",
       "      <td>1155</td>\n",
       "      <td>3296</td>\n",
       "      <td>590</td>\n",
       "      <td>769</td>\n",
       "      <td>414</td>\n",
       "      <td>148</td>\n",
       "      <td>3858</td>\n",
       "      <td>32362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>2394</td>\n",
       "      <td>462</td>\n",
       "      <td>2856</td>\n",
       "      <td>1060</td>\n",
       "      <td>3916</td>\n",
       "      <td>620</td>\n",
       "      <td>778</td>\n",
       "      <td>523</td>\n",
       "      <td>197</td>\n",
       "      <td>4636</td>\n",
       "      <td>36359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-07 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>2651</td>\n",
       "      <td>567</td>\n",
       "      <td>3218</td>\n",
       "      <td>1843</td>\n",
       "      <td>5061</td>\n",
       "      <td>1145</td>\n",
       "      <td>1247</td>\n",
       "      <td>589</td>\n",
       "      <td>233</td>\n",
       "      <td>5883</td>\n",
       "      <td>42062</td>\n",
       "      <td>pd-IT-0003</td>\n",
       "      <td>pd-EN-0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-08 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>3557</td>\n",
       "      <td>650</td>\n",
       "      <td>4207</td>\n",
       "      <td>2180</td>\n",
       "      <td>6387</td>\n",
       "      <td>1326</td>\n",
       "      <td>1492</td>\n",
       "      <td>622</td>\n",
       "      <td>366</td>\n",
       "      <td>7375</td>\n",
       "      <td>49937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>4316</td>\n",
       "      <td>733</td>\n",
       "      <td>5049</td>\n",
       "      <td>2936</td>\n",
       "      <td>7985</td>\n",
       "      <td>1598</td>\n",
       "      <td>1797</td>\n",
       "      <td>724</td>\n",
       "      <td>463</td>\n",
       "      <td>9172</td>\n",
       "      <td>53826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-10 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>5038</td>\n",
       "      <td>877</td>\n",
       "      <td>5915</td>\n",
       "      <td>2599</td>\n",
       "      <td>8514</td>\n",
       "      <td>529</td>\n",
       "      <td>977</td>\n",
       "      <td>1004</td>\n",
       "      <td>631</td>\n",
       "      <td>10149</td>\n",
       "      <td>60761</td>\n",
       "      <td>pd-IT-0005</td>\n",
       "      <td>pd-EN-0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-11 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>5838</td>\n",
       "      <td>1028</td>\n",
       "      <td>6866</td>\n",
       "      <td>3724</td>\n",
       "      <td>10590</td>\n",
       "      <td>2076</td>\n",
       "      <td>2313</td>\n",
       "      <td>1045</td>\n",
       "      <td>827</td>\n",
       "      <td>12462</td>\n",
       "      <td>73154</td>\n",
       "      <td>nd-IT-0002</td>\n",
       "      <td>nd-EN-0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-12 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>6650</td>\n",
       "      <td>1153</td>\n",
       "      <td>7803</td>\n",
       "      <td>5036</td>\n",
       "      <td>12839</td>\n",
       "      <td>2249</td>\n",
       "      <td>2651</td>\n",
       "      <td>1258</td>\n",
       "      <td>1016</td>\n",
       "      <td>15113</td>\n",
       "      <td>86011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-13 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>7426</td>\n",
       "      <td>1328</td>\n",
       "      <td>8754</td>\n",
       "      <td>6201</td>\n",
       "      <td>14955</td>\n",
       "      <td>2116</td>\n",
       "      <td>2547</td>\n",
       "      <td>1439</td>\n",
       "      <td>1266</td>\n",
       "      <td>17660</td>\n",
       "      <td>97488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>8372</td>\n",
       "      <td>1518</td>\n",
       "      <td>9890</td>\n",
       "      <td>7860</td>\n",
       "      <td>17750</td>\n",
       "      <td>2795</td>\n",
       "      <td>3497</td>\n",
       "      <td>1966</td>\n",
       "      <td>1441</td>\n",
       "      <td>21157</td>\n",
       "      <td>109170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-15 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>9663</td>\n",
       "      <td>1672</td>\n",
       "      <td>11335</td>\n",
       "      <td>9268</td>\n",
       "      <td>20603</td>\n",
       "      <td>2853</td>\n",
       "      <td>3590</td>\n",
       "      <td>2335</td>\n",
       "      <td>1809</td>\n",
       "      <td>24747</td>\n",
       "      <td>124899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>11025</td>\n",
       "      <td>1851</td>\n",
       "      <td>12876</td>\n",
       "      <td>10197</td>\n",
       "      <td>23073</td>\n",
       "      <td>2470</td>\n",
       "      <td>3233</td>\n",
       "      <td>2749</td>\n",
       "      <td>2158</td>\n",
       "      <td>27980</td>\n",
       "      <td>137962</td>\n",
       "      <td>nd-IT-0004;nd-IT-0006</td>\n",
       "      <td>nd-EN-0004;nd-EN-0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>12894</td>\n",
       "      <td>2060</td>\n",
       "      <td>14954</td>\n",
       "      <td>11108</td>\n",
       "      <td>26062</td>\n",
       "      <td>2989</td>\n",
       "      <td>3526</td>\n",
       "      <td>2941</td>\n",
       "      <td>2503</td>\n",
       "      <td>31506</td>\n",
       "      <td>148657</td>\n",
       "      <td>nd-IT-0009</td>\n",
       "      <td>nd-EN-0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>14363</td>\n",
       "      <td>2257</td>\n",
       "      <td>16620</td>\n",
       "      <td>12090</td>\n",
       "      <td>28710</td>\n",
       "      <td>2648</td>\n",
       "      <td>4207</td>\n",
       "      <td>4025</td>\n",
       "      <td>2978</td>\n",
       "      <td>35713</td>\n",
       "      <td>165541</td>\n",
       "      <td>nd-IT-0012;nd-IT-0014</td>\n",
       "      <td>nd-EN-0012;nd-EN-0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>15757</td>\n",
       "      <td>2498</td>\n",
       "      <td>18255</td>\n",
       "      <td>14935</td>\n",
       "      <td>33190</td>\n",
       "      <td>4480</td>\n",
       "      <td>5322</td>\n",
       "      <td>4440</td>\n",
       "      <td>3405</td>\n",
       "      <td>41035</td>\n",
       "      <td>182777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>16020</td>\n",
       "      <td>2655</td>\n",
       "      <td>18675</td>\n",
       "      <td>19185</td>\n",
       "      <td>37860</td>\n",
       "      <td>4670</td>\n",
       "      <td>5986</td>\n",
       "      <td>5129</td>\n",
       "      <td>4032</td>\n",
       "      <td>47021</td>\n",
       "      <td>206886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-21 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>17708</td>\n",
       "      <td>2857</td>\n",
       "      <td>20565</td>\n",
       "      <td>22116</td>\n",
       "      <td>42681</td>\n",
       "      <td>4821</td>\n",
       "      <td>6557</td>\n",
       "      <td>6072</td>\n",
       "      <td>4825</td>\n",
       "      <td>53578</td>\n",
       "      <td>233222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>19846</td>\n",
       "      <td>3009</td>\n",
       "      <td>22855</td>\n",
       "      <td>23783</td>\n",
       "      <td>46638</td>\n",
       "      <td>3957</td>\n",
       "      <td>5560</td>\n",
       "      <td>7024</td>\n",
       "      <td>5476</td>\n",
       "      <td>59138</td>\n",
       "      <td>258402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>20692</td>\n",
       "      <td>3204</td>\n",
       "      <td>23896</td>\n",
       "      <td>26522</td>\n",
       "      <td>50418</td>\n",
       "      <td>3780</td>\n",
       "      <td>4789</td>\n",
       "      <td>7432</td>\n",
       "      <td>6077</td>\n",
       "      <td>63927</td>\n",
       "      <td>275468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>21937</td>\n",
       "      <td>3396</td>\n",
       "      <td>25333</td>\n",
       "      <td>28697</td>\n",
       "      <td>54030</td>\n",
       "      <td>3612</td>\n",
       "      <td>5249</td>\n",
       "      <td>8326</td>\n",
       "      <td>6820</td>\n",
       "      <td>69176</td>\n",
       "      <td>296964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>23112</td>\n",
       "      <td>3489</td>\n",
       "      <td>26601</td>\n",
       "      <td>30920</td>\n",
       "      <td>57521</td>\n",
       "      <td>3491</td>\n",
       "      <td>5210</td>\n",
       "      <td>9362</td>\n",
       "      <td>7503</td>\n",
       "      <td>74386</td>\n",
       "      <td>324445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>24753</td>\n",
       "      <td>3612</td>\n",
       "      <td>28365</td>\n",
       "      <td>33648</td>\n",
       "      <td>62013</td>\n",
       "      <td>4492</td>\n",
       "      <td>6153</td>\n",
       "      <td>10361</td>\n",
       "      <td>8165</td>\n",
       "      <td>80539</td>\n",
       "      <td>361060</td>\n",
       "      <td>pd-IT-0007</td>\n",
       "      <td>pd-EN-0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>26029</td>\n",
       "      <td>3732</td>\n",
       "      <td>29761</td>\n",
       "      <td>36653</td>\n",
       "      <td>66414</td>\n",
       "      <td>4401</td>\n",
       "      <td>5959</td>\n",
       "      <td>10950</td>\n",
       "      <td>9134</td>\n",
       "      <td>86498</td>\n",
       "      <td>394079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-28 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>26676</td>\n",
       "      <td>3856</td>\n",
       "      <td>30532</td>\n",
       "      <td>39533</td>\n",
       "      <td>70065</td>\n",
       "      <td>3651</td>\n",
       "      <td>5974</td>\n",
       "      <td>12384</td>\n",
       "      <td>10023</td>\n",
       "      <td>92472</td>\n",
       "      <td>429526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-29 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>27386</td>\n",
       "      <td>3906</td>\n",
       "      <td>31292</td>\n",
       "      <td>42588</td>\n",
       "      <td>73880</td>\n",
       "      <td>3815</td>\n",
       "      <td>5217</td>\n",
       "      <td>13030</td>\n",
       "      <td>10779</td>\n",
       "      <td>97689</td>\n",
       "      <td>454030</td>\n",
       "      <td>pd-IT-0009</td>\n",
       "      <td>pd-EN-0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>27795</td>\n",
       "      <td>3981</td>\n",
       "      <td>31776</td>\n",
       "      <td>43752</td>\n",
       "      <td>75528</td>\n",
       "      <td>1648</td>\n",
       "      <td>4050</td>\n",
       "      <td>14620</td>\n",
       "      <td>11591</td>\n",
       "      <td>101739</td>\n",
       "      <td>477359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>28192</td>\n",
       "      <td>4023</td>\n",
       "      <td>32215</td>\n",
       "      <td>45420</td>\n",
       "      <td>77635</td>\n",
       "      <td>2107</td>\n",
       "      <td>4053</td>\n",
       "      <td>15729</td>\n",
       "      <td>12428</td>\n",
       "      <td>105792</td>\n",
       "      <td>506968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>28403</td>\n",
       "      <td>4035</td>\n",
       "      <td>32438</td>\n",
       "      <td>48134</td>\n",
       "      <td>80572</td>\n",
       "      <td>2937</td>\n",
       "      <td>4782</td>\n",
       "      <td>16847</td>\n",
       "      <td>13155</td>\n",
       "      <td>110574</td>\n",
       "      <td>541423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    country  hospitalized   ICU  hospitalized_total  \\\n",
       "date                                                                  \n",
       "2020-02-24 18:00:00     ITA           101    26                 127   \n",
       "2020-02-25 18:00:00     ITA           114    35                 150   \n",
       "2020-02-26 18:00:00     ITA           128    36                 164   \n",
       "2020-02-27 18:00:00     ITA           248    56                 304   \n",
       "2020-02-28 18:00:00     ITA           345    64                 409   \n",
       "2020-02-29 18:00:00     ITA           401   105                 506   \n",
       "2020-03-01 18:00:00     ITA           639   140                 779   \n",
       "2020-03-02 18:00:00     ITA           742   166                 908   \n",
       "2020-03-03 18:00:00     ITA          1034   229                1263   \n",
       "2020-03-04 18:00:00     ITA          1346   295                1641   \n",
       "2020-03-05 18:00:00     ITA          1790   351                2141   \n",
       "2020-03-06 18:00:00     ITA          2394   462                2856   \n",
       "2020-03-07 18:00:00     ITA          2651   567                3218   \n",
       "2020-03-08 18:00:00     ITA          3557   650                4207   \n",
       "2020-03-09 18:00:00     ITA          4316   733                5049   \n",
       "2020-03-10 18:00:00     ITA          5038   877                5915   \n",
       "2020-03-11 17:00:00     ITA          5838  1028                6866   \n",
       "2020-03-12 17:00:00     ITA          6650  1153                7803   \n",
       "2020-03-13 17:00:00     ITA          7426  1328                8754   \n",
       "2020-03-14 17:00:00     ITA          8372  1518                9890   \n",
       "2020-03-15 17:00:00     ITA          9663  1672               11335   \n",
       "2020-03-16 17:00:00     ITA         11025  1851               12876   \n",
       "2020-03-17 17:00:00     ITA         12894  2060               14954   \n",
       "2020-03-18 17:00:00     ITA         14363  2257               16620   \n",
       "2020-03-19 17:00:00     ITA         15757  2498               18255   \n",
       "2020-03-20 17:00:00     ITA         16020  2655               18675   \n",
       "2020-03-21 17:00:00     ITA         17708  2857               20565   \n",
       "2020-03-22 17:00:00     ITA         19846  3009               22855   \n",
       "2020-03-23 17:00:00     ITA         20692  3204               23896   \n",
       "2020-03-24 17:00:00     ITA         21937  3396               25333   \n",
       "2020-03-25 17:00:00     ITA         23112  3489               26601   \n",
       "2020-03-26 17:00:00     ITA         24753  3612               28365   \n",
       "2020-03-27 17:00:00     ITA         26029  3732               29761   \n",
       "2020-03-28 17:00:00     ITA         26676  3856               30532   \n",
       "2020-03-29 17:00:00     ITA         27386  3906               31292   \n",
       "2020-03-30 17:00:00     ITA         27795  3981               31776   \n",
       "2020-03-31 17:00:00     ITA         28192  4023               32215   \n",
       "2020-04-01 17:00:00     ITA         28403  4035               32438   \n",
       "\n",
       "                     home_confinement  confirmed_active  \\\n",
       "date                                                      \n",
       "2020-02-24 18:00:00                94               221   \n",
       "2020-02-25 18:00:00               162               311   \n",
       "2020-02-26 18:00:00               221               385   \n",
       "2020-02-27 18:00:00               284               588   \n",
       "2020-02-28 18:00:00               412               821   \n",
       "2020-02-29 18:00:00               543              1049   \n",
       "2020-03-01 18:00:00               798              1577   \n",
       "2020-03-02 18:00:00               927              1835   \n",
       "2020-03-03 18:00:00              1000              2263   \n",
       "2020-03-04 18:00:00              1065              2706   \n",
       "2020-03-05 18:00:00              1155              3296   \n",
       "2020-03-06 18:00:00              1060              3916   \n",
       "2020-03-07 18:00:00              1843              5061   \n",
       "2020-03-08 18:00:00              2180              6387   \n",
       "2020-03-09 18:00:00              2936              7985   \n",
       "2020-03-10 18:00:00              2599              8514   \n",
       "2020-03-11 17:00:00              3724             10590   \n",
       "2020-03-12 17:00:00              5036             12839   \n",
       "2020-03-13 17:00:00              6201             14955   \n",
       "2020-03-14 17:00:00              7860             17750   \n",
       "2020-03-15 17:00:00              9268             20603   \n",
       "2020-03-16 17:00:00             10197             23073   \n",
       "2020-03-17 17:00:00             11108             26062   \n",
       "2020-03-18 17:00:00             12090             28710   \n",
       "2020-03-19 17:00:00             14935             33190   \n",
       "2020-03-20 17:00:00             19185             37860   \n",
       "2020-03-21 17:00:00             22116             42681   \n",
       "2020-03-22 17:00:00             23783             46638   \n",
       "2020-03-23 17:00:00             26522             50418   \n",
       "2020-03-24 17:00:00             28697             54030   \n",
       "2020-03-25 17:00:00             30920             57521   \n",
       "2020-03-26 17:00:00             33648             62013   \n",
       "2020-03-27 17:00:00             36653             66414   \n",
       "2020-03-28 17:00:00             39533             70065   \n",
       "2020-03-29 17:00:00             42588             73880   \n",
       "2020-03-30 17:00:00             43752             75528   \n",
       "2020-03-31 17:00:00             45420             77635   \n",
       "2020-04-01 17:00:00             48134             80572   \n",
       "\n",
       "                     variazione_totale_positivi  confirmed_new  recovered  \\\n",
       "date                                                                        \n",
       "2020-02-24 18:00:00                           0            221          1   \n",
       "2020-02-25 18:00:00                          90             93          1   \n",
       "2020-02-26 18:00:00                          74             78          3   \n",
       "2020-02-27 18:00:00                         203            250         45   \n",
       "2020-02-28 18:00:00                         233            238         46   \n",
       "2020-02-29 18:00:00                         228            240         50   \n",
       "2020-03-01 18:00:00                         528            566         83   \n",
       "2020-03-02 18:00:00                         258            342        149   \n",
       "2020-03-03 18:00:00                         428            466        160   \n",
       "2020-03-04 18:00:00                         443            587        276   \n",
       "2020-03-05 18:00:00                         590            769        414   \n",
       "2020-03-06 18:00:00                         620            778        523   \n",
       "2020-03-07 18:00:00                        1145           1247        589   \n",
       "2020-03-08 18:00:00                        1326           1492        622   \n",
       "2020-03-09 18:00:00                        1598           1797        724   \n",
       "2020-03-10 18:00:00                         529            977       1004   \n",
       "2020-03-11 17:00:00                        2076           2313       1045   \n",
       "2020-03-12 17:00:00                        2249           2651       1258   \n",
       "2020-03-13 17:00:00                        2116           2547       1439   \n",
       "2020-03-14 17:00:00                        2795           3497       1966   \n",
       "2020-03-15 17:00:00                        2853           3590       2335   \n",
       "2020-03-16 17:00:00                        2470           3233       2749   \n",
       "2020-03-17 17:00:00                        2989           3526       2941   \n",
       "2020-03-18 17:00:00                        2648           4207       4025   \n",
       "2020-03-19 17:00:00                        4480           5322       4440   \n",
       "2020-03-20 17:00:00                        4670           5986       5129   \n",
       "2020-03-21 17:00:00                        4821           6557       6072   \n",
       "2020-03-22 17:00:00                        3957           5560       7024   \n",
       "2020-03-23 17:00:00                        3780           4789       7432   \n",
       "2020-03-24 17:00:00                        3612           5249       8326   \n",
       "2020-03-25 17:00:00                        3491           5210       9362   \n",
       "2020-03-26 17:00:00                        4492           6153      10361   \n",
       "2020-03-27 17:00:00                        4401           5959      10950   \n",
       "2020-03-28 17:00:00                        3651           5974      12384   \n",
       "2020-03-29 17:00:00                        3815           5217      13030   \n",
       "2020-03-30 17:00:00                        1648           4050      14620   \n",
       "2020-03-31 17:00:00                        2107           4053      15729   \n",
       "2020-04-01 17:00:00                        2937           4782      16847   \n",
       "\n",
       "                     deaths  confirmed   tests                note_it  \\\n",
       "date                                                                    \n",
       "2020-02-24 18:00:00       7        229    4324                    NaN   \n",
       "2020-02-25 18:00:00      10        322    8623                    NaN   \n",
       "2020-02-26 18:00:00      12        400    9587                    NaN   \n",
       "2020-02-27 18:00:00      17        650   12014                    NaN   \n",
       "2020-02-28 18:00:00      21        888   15695                    NaN   \n",
       "2020-02-29 18:00:00      29       1128   18661                    NaN   \n",
       "2020-03-01 18:00:00      34       1694   21127                    NaN   \n",
       "2020-03-02 18:00:00      52       2036   23345                    NaN   \n",
       "2020-03-03 18:00:00      79       2502   25856                    NaN   \n",
       "2020-03-04 18:00:00     107       3089   29837                    NaN   \n",
       "2020-03-05 18:00:00     148       3858   32362                    NaN   \n",
       "2020-03-06 18:00:00     197       4636   36359                    NaN   \n",
       "2020-03-07 18:00:00     233       5883   42062             pd-IT-0003   \n",
       "2020-03-08 18:00:00     366       7375   49937                    NaN   \n",
       "2020-03-09 18:00:00     463       9172   53826                    NaN   \n",
       "2020-03-10 18:00:00     631      10149   60761             pd-IT-0005   \n",
       "2020-03-11 17:00:00     827      12462   73154             nd-IT-0002   \n",
       "2020-03-12 17:00:00    1016      15113   86011                    NaN   \n",
       "2020-03-13 17:00:00    1266      17660   97488                    NaN   \n",
       "2020-03-14 17:00:00    1441      21157  109170                    NaN   \n",
       "2020-03-15 17:00:00    1809      24747  124899                    NaN   \n",
       "2020-03-16 17:00:00    2158      27980  137962  nd-IT-0004;nd-IT-0006   \n",
       "2020-03-17 17:00:00    2503      31506  148657             nd-IT-0009   \n",
       "2020-03-18 17:00:00    2978      35713  165541  nd-IT-0012;nd-IT-0014   \n",
       "2020-03-19 17:00:00    3405      41035  182777                    NaN   \n",
       "2020-03-20 17:00:00    4032      47021  206886                    NaN   \n",
       "2020-03-21 17:00:00    4825      53578  233222                    NaN   \n",
       "2020-03-22 17:00:00    5476      59138  258402                    NaN   \n",
       "2020-03-23 17:00:00    6077      63927  275468                    NaN   \n",
       "2020-03-24 17:00:00    6820      69176  296964                    NaN   \n",
       "2020-03-25 17:00:00    7503      74386  324445                    NaN   \n",
       "2020-03-26 17:00:00    8165      80539  361060             pd-IT-0007   \n",
       "2020-03-27 17:00:00    9134      86498  394079                    NaN   \n",
       "2020-03-28 17:00:00   10023      92472  429526                    NaN   \n",
       "2020-03-29 17:00:00   10779      97689  454030             pd-IT-0009   \n",
       "2020-03-30 17:00:00   11591     101739  477359                    NaN   \n",
       "2020-03-31 17:00:00   12428     105792  506968                    NaN   \n",
       "2020-04-01 17:00:00   13155     110574  541423                    NaN   \n",
       "\n",
       "                                   note_en  \n",
       "date                                        \n",
       "2020-02-24 18:00:00                    NaN  \n",
       "2020-02-25 18:00:00                    NaN  \n",
       "2020-02-26 18:00:00                    NaN  \n",
       "2020-02-27 18:00:00                    NaN  \n",
       "2020-02-28 18:00:00                    NaN  \n",
       "2020-02-29 18:00:00                    NaN  \n",
       "2020-03-01 18:00:00                    NaN  \n",
       "2020-03-02 18:00:00                    NaN  \n",
       "2020-03-03 18:00:00                    NaN  \n",
       "2020-03-04 18:00:00                    NaN  \n",
       "2020-03-05 18:00:00                    NaN  \n",
       "2020-03-06 18:00:00                    NaN  \n",
       "2020-03-07 18:00:00             pd-EN-0003  \n",
       "2020-03-08 18:00:00                    NaN  \n",
       "2020-03-09 18:00:00                    NaN  \n",
       "2020-03-10 18:00:00             pd-EN-0005  \n",
       "2020-03-11 17:00:00             nd-EN-0002  \n",
       "2020-03-12 17:00:00                    NaN  \n",
       "2020-03-13 17:00:00                    NaN  \n",
       "2020-03-14 17:00:00                    NaN  \n",
       "2020-03-15 17:00:00                    NaN  \n",
       "2020-03-16 17:00:00  nd-EN-0004;nd-EN-0006  \n",
       "2020-03-17 17:00:00             nd-EN-0009  \n",
       "2020-03-18 17:00:00  nd-EN-0012;nd-EN-0014  \n",
       "2020-03-19 17:00:00                    NaN  \n",
       "2020-03-20 17:00:00                    NaN  \n",
       "2020-03-21 17:00:00                    NaN  \n",
       "2020-03-22 17:00:00                    NaN  \n",
       "2020-03-23 17:00:00                    NaN  \n",
       "2020-03-24 17:00:00                    NaN  \n",
       "2020-03-25 17:00:00                    NaN  \n",
       "2020-03-26 17:00:00             pd-EN-0007  \n",
       "2020-03-27 17:00:00                    NaN  \n",
       "2020-03-28 17:00:00                    NaN  \n",
       "2020-03-29 17:00:00             pd-EN-0009  \n",
       "2020-03-30 17:00:00                    NaN  \n",
       "2020-03-31 17:00:00                    NaN  \n",
       "2020-04-01 17:00:00                    NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_italy = df_italy.rename(columns={'data':'date', 'stato':'country', 'ricoverati_con_sintomi':'hospitalized', 'terapia_intensiva':'ICU',\n",
    "                         'totale_ospedalizzati':'hospitalized_total',\n",
    "                         'isolamento_domiciliare':'home_confinement',\n",
    "                         'totale_positivi':'confirmed_active',\n",
    "                         'nuovi_positivi':'confirmed_new',\n",
    "                         'dimessi_guariti':'recovered',\n",
    "                         'deceduti':'deaths',\n",
    "                         'totale_casi':'confirmed',\n",
    "                         'tamponi':'tests'\n",
    "                        })\n",
    "df_italy['date'] = pd.to_datetime(df_italy.date)\n",
    "df_italy = df_italy.set_index('date')\n",
    "df_italy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>region_code</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>hospitalized</th>\n",
       "      <th>ICU</th>\n",
       "      <th>hospitalized_total</th>\n",
       "      <th>home_confinement</th>\n",
       "      <th>confirmed_active</th>\n",
       "      <th>variazione_totale_positivi</th>\n",
       "      <th>confirmed_new</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>tests</th>\n",
       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>date</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",
       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abruzzo</th>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>42.351222</td>\n",
       "      <td>13.398438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basilicata</th>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>17</td>\n",
       "      <td>40.639471</td>\n",
       "      <td>15.805148</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P.A. Bolzano</th>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calabria</th>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>18</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Campania</th>\n",
       "      <th>2020-02-24 18:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>15</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>0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toscana</th>\n",
       "      <th>2020-04-01 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>9</td>\n",
       "      <td>43.769231</td>\n",
       "      <td>11.255889</td>\n",
       "      <td>1120</td>\n",
       "      <td>297</td>\n",
       "      <td>1417</td>\n",
       "      <td>3015</td>\n",
       "      <td>4432</td>\n",
       "      <td>206</td>\n",
       "      <td>259</td>\n",
       "      <td>182</td>\n",
       "      <td>253</td>\n",
       "      <td>4867</td>\n",
       "      <td>36575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P.A. Trento</th>\n",
       "      <th>2020-04-01 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>4</td>\n",
       "      <td>46.068935</td>\n",
       "      <td>11.121231</td>\n",
       "      <td>345</td>\n",
       "      <td>76</td>\n",
       "      <td>421</td>\n",
       "      <td>1062</td>\n",
       "      <td>1483</td>\n",
       "      <td>94</td>\n",
       "      <td>124</td>\n",
       "      <td>214</td>\n",
       "      <td>173</td>\n",
       "      <td>1870</td>\n",
       "      <td>7675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Umbria</th>\n",
       "      <th>2020-04-01 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>10</td>\n",
       "      <td>43.106758</td>\n",
       "      <td>12.388247</td>\n",
       "      <td>173</td>\n",
       "      <td>45</td>\n",
       "      <td>218</td>\n",
       "      <td>646</td>\n",
       "      <td>864</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>194</td>\n",
       "      <td>37</td>\n",
       "      <td>1095</td>\n",
       "      <td>9080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valle d'Aosta</th>\n",
       "      <th>2020-04-01 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>2</td>\n",
       "      <td>45.737503</td>\n",
       "      <td>7.320149</td>\n",
       "      <td>85</td>\n",
       "      <td>27</td>\n",
       "      <td>112</td>\n",
       "      <td>428</td>\n",
       "      <td>540</td>\n",
       "      <td>-12</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>59</td>\n",
       "      <td>631</td>\n",
       "      <td>1717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veneto</th>\n",
       "      <th>2020-04-01 17:00:00</th>\n",
       "      <td>ITA</td>\n",
       "      <td>5</td>\n",
       "      <td>45.434905</td>\n",
       "      <td>12.338452</td>\n",
       "      <td>1718</td>\n",
       "      <td>350</td>\n",
       "      <td>2068</td>\n",
       "      <td>6156</td>\n",
       "      <td>8224</td>\n",
       "      <td>374</td>\n",
       "      <td>470</td>\n",
       "      <td>902</td>\n",
       "      <td>499</td>\n",
       "      <td>9625</td>\n",
       "      <td>112746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>798 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  country  region_code        lat       long  \\\n",
       "region        date                                                             \n",
       "Abruzzo       2020-02-24 18:00:00     ITA           13  42.351222  13.398438   \n",
       "Basilicata    2020-02-24 18:00:00     ITA           17  40.639471  15.805148   \n",
       "P.A. Bolzano  2020-02-24 18:00:00     ITA            4  46.499335  11.356624   \n",
       "Calabria      2020-02-24 18:00:00     ITA           18  38.905976  16.594402   \n",
       "Campania      2020-02-24 18:00:00     ITA           15  40.839566  14.250850   \n",
       "...                                   ...          ...        ...        ...   \n",
       "Toscana       2020-04-01 17:00:00     ITA            9  43.769231  11.255889   \n",
       "P.A. Trento   2020-04-01 17:00:00     ITA            4  46.068935  11.121231   \n",
       "Umbria        2020-04-01 17:00:00     ITA           10  43.106758  12.388247   \n",
       "Valle d'Aosta 2020-04-01 17:00:00     ITA            2  45.737503   7.320149   \n",
       "Veneto        2020-04-01 17:00:00     ITA            5  45.434905  12.338452   \n",
       "\n",
       "                                   hospitalized  ICU  hospitalized_total  \\\n",
       "region        date                                                         \n",
       "Abruzzo       2020-02-24 18:00:00             0    0                   0   \n",
       "Basilicata    2020-02-24 18:00:00             0    0                   0   \n",
       "P.A. Bolzano  2020-02-24 18:00:00             0    0                   0   \n",
       "Calabria      2020-02-24 18:00:00             0    0                   0   \n",
       "Campania      2020-02-24 18:00:00             0    0                   0   \n",
       "...                                         ...  ...                 ...   \n",
       "Toscana       2020-04-01 17:00:00          1120  297                1417   \n",
       "P.A. Trento   2020-04-01 17:00:00           345   76                 421   \n",
       "Umbria        2020-04-01 17:00:00           173   45                 218   \n",
       "Valle d'Aosta 2020-04-01 17:00:00            85   27                 112   \n",
       "Veneto        2020-04-01 17:00:00          1718  350                2068   \n",
       "\n",
       "                                   home_confinement  confirmed_active  \\\n",
       "region        date                                                      \n",
       "Abruzzo       2020-02-24 18:00:00                 0                 0   \n",
       "Basilicata    2020-02-24 18:00:00                 0                 0   \n",
       "P.A. Bolzano  2020-02-24 18:00:00                 0                 0   \n",
       "Calabria      2020-02-24 18:00:00                 0                 0   \n",
       "Campania      2020-02-24 18:00:00                 0                 0   \n",
       "...                                             ...               ...   \n",
       "Toscana       2020-04-01 17:00:00              3015              4432   \n",
       "P.A. Trento   2020-04-01 17:00:00              1062              1483   \n",
       "Umbria        2020-04-01 17:00:00               646               864   \n",
       "Valle d'Aosta 2020-04-01 17:00:00               428               540   \n",
       "Veneto        2020-04-01 17:00:00              6156              8224   \n",
       "\n",
       "                                   variazione_totale_positivi  confirmed_new  \\\n",
       "region        date                                                             \n",
       "Abruzzo       2020-02-24 18:00:00                           0              0   \n",
       "Basilicata    2020-02-24 18:00:00                           0              0   \n",
       "P.A. Bolzano  2020-02-24 18:00:00                           0              0   \n",
       "Calabria      2020-02-24 18:00:00                           0              0   \n",
       "Campania      2020-02-24 18:00:00                           0              0   \n",
       "...                                                       ...            ...   \n",
       "Toscana       2020-04-01 17:00:00                         206            259   \n",
       "P.A. Trento   2020-04-01 17:00:00                          94            124   \n",
       "Umbria        2020-04-01 17:00:00                          13             17   \n",
       "Valle d'Aosta 2020-04-01 17:00:00                         -12              3   \n",
       "Veneto        2020-04-01 17:00:00                         374            470   \n",
       "\n",
       "                                   recovered  deaths  confirmed   tests  \\\n",
       "region        date                                                        \n",
       "Abruzzo       2020-02-24 18:00:00          0       0          0       5   \n",
       "Basilicata    2020-02-24 18:00:00          0       0          0       0   \n",
       "P.A. Bolzano  2020-02-24 18:00:00          0       0          0       1   \n",
       "Calabria      2020-02-24 18:00:00          0       0          0       1   \n",
       "Campania      2020-02-24 18:00:00          0       0          0      10   \n",
       "...                                      ...     ...        ...     ...   \n",
       "Toscana       2020-04-01 17:00:00        182     253       4867   36575   \n",
       "P.A. Trento   2020-04-01 17:00:00        214     173       1870    7675   \n",
       "Umbria        2020-04-01 17:00:00        194      37       1095    9080   \n",
       "Valle d'Aosta 2020-04-01 17:00:00         32      59        631    1717   \n",
       "Veneto        2020-04-01 17:00:00        902     499       9625  112746   \n",
       "\n",
       "                                  note_it note_en  \n",
       "region        date                                 \n",
       "Abruzzo       2020-02-24 18:00:00     NaN     NaN  \n",
       "Basilicata    2020-02-24 18:00:00     NaN     NaN  \n",
       "P.A. Bolzano  2020-02-24 18:00:00     NaN     NaN  \n",
       "Calabria      2020-02-24 18:00:00     NaN     NaN  \n",
       "Campania      2020-02-24 18:00:00     NaN     NaN  \n",
       "...                                   ...     ...  \n",
       "Toscana       2020-04-01 17:00:00     NaN     NaN  \n",
       "P.A. Trento   2020-04-01 17:00:00     NaN     NaN  \n",
       "Umbria        2020-04-01 17:00:00     NaN     NaN  \n",
       "Valle d'Aosta 2020-04-01 17:00:00     NaN     NaN  \n",
       "Veneto        2020-04-01 17:00:00     NaN     NaN  \n",
       "\n",
       "[798 rows x 17 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_italy_regions = pd.read_csv('https://github.com/pcm-dpc/COVID-19/raw/master/dati-regioni/dpc-covid19-ita-regioni.csv', parse_dates=['data'])\n",
    "df_italy_regions = df_italy_regions.rename(columns={'data':'date', 'stato':'country', 'ricoverati_con_sintomi':'hospitalized', 'terapia_intensiva':'ICU',\n",
    "                         'totale_ospedalizzati':'hospitalized_total',\n",
    "                         'isolamento_domiciliare':'home_confinement',\n",
    "                         'totale_positivi':'confirmed_active',\n",
    "                         'nuovi_positivi':'confirmed_new',\n",
    "                         'dimessi_guariti':'recovered',\n",
    "                         'deceduti':'deaths',\n",
    "                         'totale_casi':'confirmed',\n",
    "                         'tamponi':'tests',\n",
    "                         'codice_regione':'region_code',\n",
    "                         'denominazione_regione':'region'            \n",
    "                        })\n",
    "df_italy_regions = df_italy_regions.set_index(['region','date'])\n",
    "df_italy_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2,figsize=(20,16))\n",
    "for i, ctype in enumerate(['confirmed', 'confirmed_new', 'deaths', 'tests'], 1):\n",
    "    plt.subplot(2,2,i)\n",
    "    ax = sns.barplot(data=df_italy, x=df_italy.index, y=ctype, color=str('C' + str(i-1)))\n",
    "    xticks = [xdate.strftime('%d.%m.') for xdate in df_italy.index]\n",
    "    _ =ax.set_xticklabels(xticks, rotation=45, ha='right')\n",
    "    _ = ax.set_title(ctype)\n",
    "_ = plt.suptitle('COVID-19 - confirmed cases in Italy',fontsize=20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "Lombardia                44773\n",
       "Emilia-Romagna           14787\n",
       "Piemonte                  9795\n",
       "Veneto                    9625\n",
       "Toscana                   4867\n",
       "Marche                    3962\n",
       "Liguria                   3660\n",
       "Lazio                     3264\n",
       "Campania                  2231\n",
       "Puglia                    1946\n",
       "P.A. Trento               1870\n",
       "Sicilia                   1718\n",
       "Friuli Venezia Giulia     1685\n",
       "Abruzzo                   1436\n",
       "P.A. Bolzano              1418\n",
       "Umbria                    1095\n",
       "Sardegna                   745\n",
       "Calabria                   669\n",
       "Valle d'Aosta              631\n",
       "Basilicata                 237\n",
       "Molise                     160\n",
       "Name: confirmed, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_italy_regions.groupby(level=0).confirmed.max().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e22e332848>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='bar', data=df_italy_regions.reset_index(), x='date', y='confirmed', col='region', col_wrap=4, facet_kws={'sharey':False})"
   ]
  },
  {
   "cell_type": "code",
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