diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 15a9bbd616a27a0330115a261cb80ee664835707..6c4ad5a9003c061723cd5c9c36e5b1bdb58828fb 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -7795,7 +7795,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -7819,172 +7819,214 @@ " \n", " \n", " \n", + " deaths per 1000\n", " beds per 1000\n", " \n", " \n", " LOCATION\n", " \n", + " \n", " \n", " \n", " \n", " \n", " AUT\n", + " NaN\n", " 7.37\n", " \n", " \n", " BEL\n", + " NaN\n", " 5.66\n", " \n", " \n", " CAN\n", + " NaN\n", " 2.52\n", " \n", " \n", " CZE\n", + " NaN\n", " 6.63\n", " \n", " \n", " DNK\n", + " NaN\n", " 2.61\n", " \n", " \n", " FIN\n", + " NaN\n", " 3.28\n", " \n", " \n", " FRA\n", + " NaN\n", " 5.98\n", " \n", " \n", " DEU\n", + " NaN\n", " 8.00\n", " \n", " \n", " GRC\n", + " NaN\n", " 4.21\n", " \n", " \n", " HUN\n", + " NaN\n", " 7.02\n", " \n", " \n", " ISL\n", + " NaN\n", " 3.06\n", " \n", " \n", " IRL\n", + " NaN\n", " 2.96\n", " \n", " \n", " ITA\n", + " NaN\n", " 3.18\n", " \n", " \n", " JPN\n", + " NaN\n", " 13.05\n", " \n", " \n", " KOR\n", + " NaN\n", " 12.27\n", " \n", " \n", " LUX\n", + " NaN\n", " 4.66\n", " \n", " \n", " MEX\n", + " NaN\n", " 1.38\n", " \n", " \n", " NLD\n", + " NaN\n", " 3.32\n", " \n", " \n", " NZL\n", + " NaN\n", " 2.71\n", " \n", " \n", " NOR\n", + " NaN\n", " 3.60\n", " \n", " \n", " POL\n", + " NaN\n", " 6.62\n", " \n", " \n", " PRT\n", + " NaN\n", " 3.39\n", " \n", " \n", " SVK\n", + " NaN\n", " 5.82\n", " \n", " \n", " ESP\n", + " NaN\n", " 2.97\n", " \n", " \n", " SWE\n", + " NaN\n", " 2.22\n", " \n", " \n", " CHE\n", + " NaN\n", " 4.53\n", " \n", " \n", " TUR\n", + " NaN\n", " 2.81\n", " \n", " \n", " GBR\n", + " NaN\n", " 2.54\n", " \n", " \n", " CHL\n", + " NaN\n", " 2.11\n", " \n", " \n", " CHN\n", + " NaN\n", " 4.34\n", " \n", " \n", " EST\n", + " NaN\n", " 4.69\n", " \n", " \n", " IND\n", + " NaN\n", " 0.53\n", " \n", " \n", " IDN\n", + " NaN\n", " 1.04\n", " \n", " \n", " ISR\n", + " NaN\n", " 3.02\n", " \n", " \n", " RUS\n", + " NaN\n", " 8.05\n", " \n", " \n", " SVN\n", + " NaN\n", " 4.50\n", " \n", " \n", " COL\n", + " NaN\n", " 1.70\n", " \n", " \n", " LVA\n", + " NaN\n", " 5.57\n", " \n", " \n", " LTU\n", + " NaN\n", " 6.56\n", " \n", " \n", " CRI\n", + " NaN\n", " 1.13\n", " \n", " \n", @@ -7992,57 +8034,57 @@ "" ], "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')" + "" ] }, - "execution_count": 79, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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." ] }, {