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module3/exo3/exercice.ipynb
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......@@ -7795,7 +7795,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 98,
"metadata": {},
"outputs": [
{
......@@ -7819,172 +7819,214 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>deaths per 1000</th>\n",
" <th>beds per 1000</th>\n",
" </tr>\n",
" <tr>\n",
" <th>LOCATION</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AUT</th>\n",
" <td>NaN</td>\n",
" <td>7.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BEL</th>\n",
" <td>NaN</td>\n",
" <td>5.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CAN</th>\n",
" <td>NaN</td>\n",
" <td>2.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CZE</th>\n",
" <td>NaN</td>\n",
" <td>6.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DNK</th>\n",
" <td>NaN</td>\n",
" <td>2.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FIN</th>\n",
" <td>NaN</td>\n",
" <td>3.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FRA</th>\n",
" <td>NaN</td>\n",
" <td>5.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DEU</th>\n",
" <td>NaN</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GRC</th>\n",
" <td>NaN</td>\n",
" <td>4.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HUN</th>\n",
" <td>NaN</td>\n",
" <td>7.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ISL</th>\n",
" <td>NaN</td>\n",
" <td>3.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IRL</th>\n",
" <td>NaN</td>\n",
" <td>2.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ITA</th>\n",
" <td>NaN</td>\n",
" <td>3.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>JPN</th>\n",
" <td>NaN</td>\n",
" <td>13.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KOR</th>\n",
" <td>NaN</td>\n",
" <td>12.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LUX</th>\n",
" <td>NaN</td>\n",
" <td>4.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEX</th>\n",
" <td>NaN</td>\n",
" <td>1.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NLD</th>\n",
" <td>NaN</td>\n",
" <td>3.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NZL</th>\n",
" <td>NaN</td>\n",
" <td>2.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NOR</th>\n",
" <td>NaN</td>\n",
" <td>3.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>POL</th>\n",
" <td>NaN</td>\n",
" <td>6.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRT</th>\n",
" <td>NaN</td>\n",
" <td>3.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVK</th>\n",
" <td>NaN</td>\n",
" <td>5.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ESP</th>\n",
" <td>NaN</td>\n",
" <td>2.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SWE</th>\n",
" <td>NaN</td>\n",
" <td>2.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHE</th>\n",
" <td>NaN</td>\n",
" <td>4.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TUR</th>\n",
" <td>NaN</td>\n",
" <td>2.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GBR</th>\n",
" <td>NaN</td>\n",
" <td>2.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHL</th>\n",
" <td>NaN</td>\n",
" <td>2.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHN</th>\n",
" <td>NaN</td>\n",
" <td>4.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EST</th>\n",
" <td>NaN</td>\n",
" <td>4.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IND</th>\n",
" <td>NaN</td>\n",
" <td>0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IDN</th>\n",
" <td>NaN</td>\n",
" <td>1.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ISR</th>\n",
" <td>NaN</td>\n",
" <td>3.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RUS</th>\n",
" <td>NaN</td>\n",
" <td>8.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVN</th>\n",
" <td>NaN</td>\n",
" <td>4.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COL</th>\n",
" <td>NaN</td>\n",
" <td>1.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LVA</th>\n",
" <td>NaN</td>\n",
" <td>5.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LTU</th>\n",
" <td>NaN</td>\n",
" <td>6.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRI</th>\n",
" <td>NaN</td>\n",
" <td>1.13</td>\n",
" </tr>\n",
" </tbody>\n",
......@@ -7992,57 +8034,57 @@
"</div>"
],
"text/plain": [
" beds per 1000\n",
"LOCATION \n",
"AUT 7.37\n",
"BEL 5.66\n",
"CAN 2.52\n",
"CZE 6.63\n",
"DNK 2.61\n",
"FIN 3.28\n",
"FRA 5.98\n",
"DEU 8.00\n",
"GRC 4.21\n",
"HUN 7.02\n",
"ISL 3.06\n",
"IRL 2.96\n",
"ITA 3.18\n",
"JPN 13.05\n",
"KOR 12.27\n",
"LUX 4.66\n",
"MEX 1.38\n",
"NLD 3.32\n",
"NZL 2.71\n",
"NOR 3.60\n",
"POL 6.62\n",
"PRT 3.39\n",
"SVK 5.82\n",
"ESP 2.97\n",
"SWE 2.22\n",
"CHE 4.53\n",
"TUR 2.81\n",
"GBR 2.54\n",
"CHL 2.11\n",
"CHN 4.34\n",
"EST 4.69\n",
"IND 0.53\n",
"IDN 1.04\n",
"ISR 3.02\n",
"RUS 8.05\n",
"SVN 4.50\n",
"COL 1.70\n",
"LVA 5.57\n",
"LTU 6.56\n",
"CRI 1.13"
" deaths per 1000 beds per 1000\n",
"LOCATION \n",
"AUT NaN 7.37\n",
"BEL NaN 5.66\n",
"CAN NaN 2.52\n",
"CZE NaN 6.63\n",
"DNK NaN 2.61\n",
"FIN NaN 3.28\n",
"FRA NaN 5.98\n",
"DEU NaN 8.00\n",
"GRC NaN 4.21\n",
"HUN NaN 7.02\n",
"ISL NaN 3.06\n",
"IRL NaN 2.96\n",
"ITA NaN 3.18\n",
"JPN NaN 13.05\n",
"KOR NaN 12.27\n",
"LUX NaN 4.66\n",
"MEX NaN 1.38\n",
"NLD NaN 3.32\n",
"NZL NaN 2.71\n",
"NOR NaN 3.60\n",
"POL NaN 6.62\n",
"PRT NaN 3.39\n",
"SVK NaN 5.82\n",
"ESP NaN 2.97\n",
"SWE NaN 2.22\n",
"CHE NaN 4.53\n",
"TUR NaN 2.81\n",
"GBR NaN 2.54\n",
"CHL NaN 2.11\n",
"CHN NaN 4.34\n",
"EST NaN 4.69\n",
"IND NaN 0.53\n",
"IDN NaN 1.04\n",
"ISR NaN 3.02\n",
"RUS NaN 8.05\n",
"SVN NaN 4.50\n",
"COL NaN 1.70\n",
"LVA NaN 5.57\n",
"LTU NaN 6.56\n",
"CRI NaN 1.13"
]
},
"execution_count": 46,
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beds_vs_deaths = pd.DataFrame(index=hospital_beds['LOCATION'])\n",
"beds_vs_deaths = pd.DataFrame(index=hospital_beds['LOCATION'], columns=(\"deaths per 1000\",))\n",
"beds_vs_deaths['beds per 1000'] = hospital_beds['Value']\n",
"beds_vs_deaths"
]
......@@ -8056,105 +8098,88 @@
},
{
"cell_type": "code",
"execution_count": 81,
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Belgium\n",
"2020-03-07 00:00:00\n",
"2020-04-06 00:00:00\n",
"China (all provinces except Hong Kong)\n",
"2020-01-22 00:00:00\n",
"2020-02-21 00:00:00\n",
"China\n",
"2020-01-22 00:00:00\n",
"2020-02-21 00:00:00\n",
"Hong-Kong\n",
"2020-03-16 00:00:00\n",
"2020-04-15 00:00:00\n",
"France except Dom/Tom\n",
"2020-03-02 00:00:00\n",
"2020-04-01 00:00:00\n",
"Germany\n",
"2020-03-02 00:00:00\n",
"2020-04-01 00:00:00\n",
"Iran\n",
"2020-02-27 00:00:00\n",
"2020-03-28 00:00:00\n",
"Italy\n",
"2020-02-23 00:00:00\n",
"2020-03-24 00:00:00\n",
"Japan\n",
"2020-02-24 00:00:00\n",
"2020-03-25 00:00:00\n",
"Korea South\n",
"2020-02-21 00:00:00\n",
"2020-03-22 00:00:00\n",
"Netherlands without the colonies\n",
"2020-03-07 00:00:00\n",
"2020-04-06 00:00:00\n",
"Portugal\n",
"2020-03-14 00:00:00\n",
"2020-04-13 00:00:00\n",
"Spain\n",
"2020-03-03 00:00:00\n",
"2020-04-02 00:00:00\n",
"United Kingdom without the colonies\n",
"2020-03-06 00:00:00\n",
"2020-04-05 00:00:00\n",
"US\n",
"2020-03-05 00:00:00\n",
"2020-04-04 00:00:00\n"
]
}
],
"outputs": [],
"source": [
"deaths_per_1000_by_country = {}\n",
"for region, data in to_plot.items(): # something is wrong here!\n",
" print(region)\n",
" row = data[data[\"confirmed cases\"] >= 150].iloc[0]\n",
" print(row.name)\n",
" date_to_look_at = row.name + datetime.timedelta(days=30)\n",
" print(date_to_look_at)\n",
" \n",
"# col = data.columns[0] \n",
"# for i, val in enumerate(data[\"confirmed cases\"]):\n",
"# #print(val)\n",
"# if val >= 150:\n",
"# cutpoint = i \n",
"# break\n",
"# assert cutpoint != -1\n",
"# # print(cutpoint)\n",
"# # print(\"-------\")\n",
"# # print(region)\n",
"# # print(to_plot[region][:])\n",
"# # print(\",,,,\")\n",
"# # print(data.iloc[cutpoint:cutpoint+5])\n",
"# deaths_per_1000_by_country[region] = \n",
"# to_plot_cut\n"
"country_names = list(to_plot.keys())\n",
"country_names\n",
"country_codes = ['BEL',\n",
" '', # china no hong kong\n",
" 'CHN',\n",
" '', # hong kong\n",
" 'FRA',\n",
" 'DEU',\n",
" '', # iran\n",
" 'ITA',\n",
" 'JPN',\n",
" 'KOR',\n",
" 'NLD',\n",
" 'PRT',\n",
" 'ESP',\n",
" 'GBR',\n",
" 'USA']\n",
"country_name_to_code = {na: co for na, co in zip(country_names, country_codes)}\n",
"#country_name_to_code"
]
},
{
"cell_type": "code",
"execution_count": 79,
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2020-03-05 00:00:00')"
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8ed8c17160>"
]
},
"execution_count": 79,
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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yxsFvZpYxDn4zs4xx8JuZZYyD3wrCC9+b9V0ltRCL9Q1e+N6sb/OI3/LOC9+b9W0Ofss7L3xv1rd5qsfyzgvfm/VtDn4rCC98b9Z3earHzCxjHPxmZhnj4DczyxgHv5lZxjj4zcwyxsFvZpYxqYJf0jxJGyXVS7qqk/2SdF2y/0VJ09vte1XSS5Kel1Sbz+J7m+8/Y2aloMvP8UsaCNwAnAg0AOskrY2I9e2anQRMSr5mATcm31sdFxFv5a3qIvD9Z8ysVKQZ8c8E6iNic0Q0AauB+R3azAdWRk4NMEzSYXmutah8/xkzKxVpgn8UsK3d84ZkW9o2ATwkqU7S4n0ttNh8/xkzKxVpbtmgTrZFN9p8JSJel3Qo8LCkVyLi8c8cJPefwmKAMWPGpCird/n+M2ZWKtIEfwMwut3zCuD1tG0iovX7m5LuITd19Jngj4hlwDKAqqqqjv+x9Am+/4yZlYI0Uz3rgEmSxksaDCwC1nZosxY4O/l0TzXwbkTskHSgpKEAkg4E5gB/yGP9ZmbWTV2O+COiWdLFwIPAQOCmiHhZ0gXJ/qXAfcDJQD3wIfDNpPtfA/dIaj3WbRHxQN7PwszMUlNE35tVqaqqitrafv2RfzOzXiWpLiKq0rT1lbtmZhnj4DczyxgHv5lZxjj4zcwyxsFvZpYxDn4zs4xx8JuZZYyD38wsYxz8ZmYZ4+A3M8sYB7+ZWcY4+M3MMsbBb2aWMQ5+M7OMcfCbmWWMg9/MLGMc/GZmGePgNzPLGAe/mVnGOPjNzDLGwW9mljEOfjOzjHHwm5lljIPfzCxjHPxmZhnj4DczyxgHv5lZxjj4zXqobusubni0nrqtu4pdilkqZcUuwKw/q9u6izOX19DU3MLgsgGsOr+ayrHDi12W2efyiN+sB2o2N9LU3EJLwO7mFmo2Nxa7JLMuOfjNeqB6QjmDywYwUDCobADVE8qLXZJZlzzVY9YDlWOHs+r8amo2N1I9odzTPNYvOPjNeqhy7HAHvvUrnuoxM8sYB7+ZWcY4+M3MuqEUrttIFfyS5knaKKle0lWd7Jek65L9L0qanrav7V0p/ICZlZLW6zaufWgjZy6v6bf/Nrt8c1fSQOAG4ESgAVgnaW1ErG/X7CRgUvI1C7gRmJWyr3XCFwaZ9T2dXbeRr3+XdVt39dqnw9J8qmcmUB8RmwEkrQbmA+3Dez6wMiICqJE0TNJhwLgUfa0ThfwBM7N903rdxu7mlrxet9HbA700wT8K2NbueQO5UX1XbUal7AuApMXAYoAxY8akKKu0FeoHzMz2XaGu2+jtgV6a4Fcn2yJlmzR9cxsjlgHLAKqqqjptkyW+MMisbyrEdRu9PdBLE/wNwOh2zyuA11O2GZyir+2FLwwyy4beHuilCf51wCRJ44HtwCLgbzu0WQtcnMzhzwLejYgdknam6Gtmlnm9OdDrMvgjolnSxcCDwEDgpoh4WdIFyf6lwH3AyUA98CHwzc/rW5AzMTOzVJT7IE7fUlVVFbW1tcUuw8ys35BUFxFVadr6yl0zs4xx8JuZZYyD38wsYxz8ZmYZ0+cWYkmu4H1L0tZi11JgI4C3il1EL/B5lhafZ981Nm3DPvepHkm1ad+Z7s98nqXF51laSv08PdVjZpYxDn4zs4zpi8G/rNgF9BKfZ2nxeZaWkj7PPjfHb2ZmhdUXR/xmZlZAfSb4JY2W9KikDZJelnRZsWsqFEkDJf1e0q+KXUshJSux3SXpleTv9cvFrinfJC1Jfl7/IOl2SUOKXVO+SLpJ0puS/tBu219JeljSpuR7v75v+F7O8SfJz+yLku6RNKyYNRZCnwl+oBn4+4j4ElANXCRpSpFrKpTLgA3FLqIX/CvwQER8ETiSEjtnSaOAS4GqiDiC3B1oFxW3qrz6BTCvw7argN9ExCTgN8nz/uwXfPYcHwaOiIipwH8A/9DbRRVanwn+iNgREc8lj98nFxKjiltV/kmqAP47sLzYtRSSpIOB2cAKgIhoioh3iltVQZQB+0sqAw6ghBYaiojHgbc7bJ4P3JI8vgU4rVeLyrPOzjEiHoqI5uRpDbkFpEpKnwn+9iSNA44CniluJQXxf4H/CbQUu5ACmwDsBG5OprWWSzqw2EXlU0RsB64BXgN2kFuA6KHiVlVwfx0ROyA3WAMOLXI9hXYucH+xi8i3Phf8kg4C/h9weUS8V+x68knSKcCbEVFX7Fp6QRkwHbgxIo4C/kz/nxb4lGR+ez4wHvgCcKCkvytuVZYvkr5Lbgp6VbFrybc+FfySBpEL/VURcXex6ymArwCnSnoVWA38N0n/XtySCqYBaIiI1t/a7iL3H0EpOQHYEhE7I2I3cDdwdJFrKrQ3JB0GkHx/s8j1FISkc4BTgDOjBD/z3meCX5LIzQdviIh/KXY9hRAR/xARFRExjtybgI9EREmOECPiT8A2SZOTTccD64tYUiG8BlRLOiD5+T2eEnsDuxNrgXOSx+cAa4pYS0FImgdcCZwaER8Wu55C6DPBT240fBa5UfDzydfJxS7KeuQSYJWkF4FpwP8pcj15lfw2cxfwHPASuX9PJXPFp6TbgaeByZIaJJ0H/BA4UdIm4MTkeb+1l3O8HhgKPJzk0NKiFlkAvnLXzCxj+tKI38zMeoGD38wsYxz8ZmYZ4+A3M8sYB7+ZWcY4+K1PkzSu/Z0Te7t/T0gqT+44+4Gk6zvsq5T0kqR6Sdcl1wEgaT9JdyTbn0luX9La55zkrpibkguMzPaJg98sT5IbtbX3EfC/gCs6aX4jsBiYlHy13iHyPGBXREwEfgr8KHntvwL+EZgFzAT+sb/fEtmKx8Fv/UGZpFuS+6PfJekAaBs1/1ZSnaQH291KoFLSC5KeBi5qfRFJh0t6Nrko50VJkzoeKBmdXyvpOUm/kTQy2f6fJD2QHOsJSV9Mtv9C0r9IepQkpFtFxJ8j4kly/wG0P8ZhwMER8XRyO4CVfHKXy/Z3v7wLOD75bWAu8HBEvB0Ru8jdOrjj7YTNUnHwW38wGViW3B/9PeDbyX2dfgYsiIhK4CbgB0n7m4FLI6Ljwi8XAP8aEdOAKnL3E+roQOC5iJgO/JbcKBtyV+RekhzrCuDf2vX5z8AJEfH3Kc9nVIdjN/DJLchHAdsAklsDvwuUt9/eSR+zbun4q6lZX7QtIn6XPP53coufPAAcQe6yesgtgrJD0iHAsIj4bdL+VuCk5PHTwHeTNRHujohNnRyrBbij3bHuTu4YezRwZ3IsgP3a9bkzIvZ043zUybboYt/n9THrFge/9QcdA641CF/uOKpPlsnrNBAj4jZJz5BbCOdBSedHxCMpjj0AeCf5TaEzf+7qBDpo4NOLe1TwyQIuDcBooCF5z+AQcguFNAD/tUOfx7p5XDPAUz3WP4zRJ+v1ngE8CWwERrZulzRI0uHJKl/vSjomaX9m64tImgBsjojryN1lcmonxxoALEge/y3wZLIuxBZJpyevI0lH7uvJJAuYvC+pOpm/P5tP7nLZ/u6XC8jdwTWAB4E5koYnb+rOSbaZdZtH/NYfbADOkfRzYBO5xV2aJC0Arkumd8rIrW72MvBN4CZJH/LpcFwI/J2k3cCfgH/q5Fh/Bg6XVEdufn1hsv1M4EZJ3wMGkVtP4YWuCk/WXjgYGCzpNGBORKwHLiS33uv+5FZ4al3laQVwq6R6ciP9RQAR8bakfwbWJe3+KSI6LotolorvzmnWjqQPIuKgYtdhVkie6jEzyxiP+M3MMsYjfjOzjHHwm5lljIPfzCxjHPxmZhnj4DczyxgHv5lZxvx/cdd1v4cNa+4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"row.name"
"for region, data in to_plot.items(): # something is wrong here!\n",
"# print(region)\n",
" row = data[data[\"confirmed cases\"] >= 150].iloc[0]\n",
"# print(row.name)\n",
" date_to_look_at = row.name + datetime.timedelta(days=30)\n",
"# print(date_to_look_at)\n",
" good_row = to_plot_deaths[region][to_plot_deaths[region].index == date_to_look_at]\n",
"# print(good_row)\n",
" dc = good_row[\"deaths cases\"][0]\n",
"# print(dc)\n",
" co = country_name_to_code[region]\n",
" if co == '':\n",
" continue\n",
" pop = 1.e6 / 1000 * population[population.index == co]['Value'][0]\n",
"# print(pop)\n",
" beds_vs_deaths.loc[co, 'deaths per 1000'] = dc / pop\n",
"# xs = beds_vs_deaths[\"deaths per 1000\"]\n",
"# ys = beds_vs_deaths[\"beds per 1000\"]\n",
"# plt.scatter(xs, ys)\n",
"# plt.show()\n",
"beds_vs_deaths.plot(\"beds per 1000\", \"deaths per 1000\", style='.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for the selected countries we see that after having at least 8 beds per 1000 inhabitants the death rate is very close to 0. One could now verify if this holds true for all the other countries too."
]
},
{
......
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