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Commit 55c6925c authored by 678362a9c2b5d46a6a161a762a6e18e0's avatar 678362a9c2b5d46a6a161a762a6e18e0
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module3/exo3/exercice.ipynb
View file @ 55c6925c
...@@ -404,37 +404,13 @@ ...@@ -404,37 +404,13 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Enregistrons les lignes consernées dans un nouveau tableau en ajoutant la Chine (nous utiliserons les coordonnées de Beijing pour la longitude et latitude de la Chine, même si l'information ne sera pas réutilisée pour le moment plus tard) :" "Enregistrons les lignes consernées dans un nouveau tableau en ajoutant la Chine et Hong-Kong. Profitons en pour définir la localisation comme index et supprimons les colonnes inutiles et trions le tableau."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [
"data_list = []\n",
"# récupération des données pour tous les pays sauf la Chine\n",
"for country in countries:\n",
" data_list.append(raw_data[(raw_data['Province/State'].isnull()) & (raw_data['Country/Region']==country)].values.tolist()[0])\n",
"# récupération des données pour Hong-Kong\n",
"data_list.append(raw_data[(raw_data['Province/State'] == \"Hong Kong\") & (raw_data['Country/Region']== \"China\")].values.tolist()[0])\n",
"# récupération des données pour le reste de la Chine en sommant les différentes colonnes\n",
"data_china = raw_data[(raw_data['Province/State'] != \"Hong Kong\") & (raw_data['Country/Region']== \"China\")].sum().values.tolist()\n",
"# mise à jour des premières colonnes des données de la Chine avec les coordonées de Beijing et ajout à la liste\n",
"data_china[0] = pd.np.NaN\n",
"data_china[1] = \"China\"\n",
"data_china[2] = 40.1824\n",
"data_china[3] = 116.4142\n",
"data_list.append(data_china)\n",
"# création du nouveau tableau avec toutes les données des pays recherchés\n",
"data = pd.DataFrame(data_list, columns=raw_data.columns)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -457,18 +433,17 @@ ...@@ -457,18 +433,17 @@
" <thead>\n", " <thead>\n",
" <tr style=\"text-align: right;\">\n", " <tr style=\"text-align: right;\">\n",
" <th></th>\n", " <th></th>\n",
" <th>Province/State</th>\n",
" <th>Country/Region</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" <th>1/22/20</th>\n", " <th>1/22/20</th>\n",
" <th>1/23/20</th>\n", " <th>1/23/20</th>\n",
" <th>1/24/20</th>\n", " <th>1/24/20</th>\n",
" <th>1/25/20</th>\n", " <th>1/25/20</th>\n",
" <th>1/26/20</th>\n", " <th>1/26/20</th>\n",
" <th>1/27/20</th>\n", " <th>1/27/20</th>\n",
" <th>1/28/20</th>\n",
" <th>1/29/20</th>\n",
" <th>1/30/20</th>\n",
" <th>1/31/20</th>\n",
" <th>...</th>\n", " <th>...</th>\n",
" <th>2/23/21</th>\n",
" <th>2/24/21</th>\n", " <th>2/24/21</th>\n",
" <th>2/25/21</th>\n", " <th>2/25/21</th>\n",
" <th>2/26/21</th>\n", " <th>2/26/21</th>\n",
...@@ -478,15 +453,160 @@ ...@@ -478,15 +453,160 @@
" <th>3/2/21</th>\n", " <th>3/2/21</th>\n",
" <th>3/3/21</th>\n", " <th>3/3/21</th>\n",
" <th>3/4/21</th>\n", " <th>3/4/21</th>\n",
" <th>Sum</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Localisation</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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n", " </tr>\n",
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>0</th>\n", " <th>Hong-Kong</th>\n",
" <td>NaN</td>\n", " <td>0</td>\n",
" <td>Belgium</td>\n", " <td>2</td>\n",
" <td>50.833300</td>\n", " <td>2</td>\n",
" <td>4.469936</td>\n", " <td>5</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>10913</td>\n",
" <td>10926</td>\n",
" <td>10950</td>\n",
" <td>10983</td>\n",
" <td>11005</td>\n",
" <td>11019</td>\n",
" <td>11032</td>\n",
" <td>11046</td>\n",
" <td>11055</td>\n",
" <td>1659701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Korea, South</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>88516</td>\n",
" <td>88922</td>\n",
" <td>89321</td>\n",
" <td>89676</td>\n",
" <td>90031</td>\n",
" <td>90372</td>\n",
" <td>90816</td>\n",
" <td>91240</td>\n",
" <td>91638</td>\n",
" <td>10964482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>548</td>\n",
" <td>641</td>\n",
" <td>918</td>\n",
" <td>1401</td>\n",
" <td>2067</td>\n",
" <td>2869</td>\n",
" <td>5501</td>\n",
" <td>6077</td>\n",
" <td>8131</td>\n",
" <td>9790</td>\n",
" <td>...</td>\n",
" <td>89919</td>\n",
" <td>89925</td>\n",
" <td>89935</td>\n",
" <td>89941</td>\n",
" <td>89960</td>\n",
" <td>89971</td>\n",
" <td>89981</td>\n",
" <td>89991</td>\n",
" <td>90000</td>\n",
" <td>33057311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>...</td>\n",
" <td>427732</td>\n",
" <td>428816</td>\n",
" <td>429873</td>\n",
" <td>431093</td>\n",
" <td>432090</td>\n",
" <td>432778</td>\n",
" <td>433700</td>\n",
" <td>434944</td>\n",
" <td>436093</td>\n",
" <td>41743265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Portugal</th>\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>...</td>\n",
" <td>800586</td>\n",
" <td>801746</td>\n",
" <td>802773</td>\n",
" <td>803844</td>\n",
" <td>804562</td>\n",
" <td>804956</td>\n",
" <td>805647</td>\n",
" <td>806626</td>\n",
" <td>807456</td>\n",
" <td>70635994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\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",
...@@ -494,7 +614,6 @@ ...@@ -494,7 +614,6 @@
" <td>0</td>\n", " <td>0</td>\n",
" <td>0</td>\n", " <td>0</td>\n",
" <td>...</td>\n", " <td>...</td>\n",
" <td>757696</td>\n",
" <td>760809</td>\n", " <td>760809</td>\n",
" <td>763885</td>\n", " <td>763885</td>\n",
" <td>766654</td>\n", " <td>766654</td>\n",
...@@ -504,61 +623,38 @@ ...@@ -504,61 +623,38 @@
" <td>774344</td>\n", " <td>774344</td>\n",
" <td>777608</td>\n", " <td>777608</td>\n",
" <td>780251</td>\n", " <td>780251</td>\n",
" <td>98653796</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>1</th>\n", " <th>Netherlands</th>\n",
" <td>NaN</td>\n", " <td>0</td>\n",
" <td>France</td>\n", " <td>0</td>\n",
" <td>46.227600</td>\n", " <td>0</td>\n",
" <td>2.213700</td>\n",
" <td>0</td>\n", " <td>0</td>\n",
" <td>0</td>\n", " <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3608271</td>\n",
" <td>3639501</td>\n",
" <td>3664050</td>\n",
" <td>3689034</td>\n",
" <td>3712474</td>\n",
" <td>3732426</td>\n",
" <td>3736390</td>\n",
" <td>3759247</td>\n",
" <td>3785326</td>\n",
" <td>3810605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>Germany</td>\n",
" <td>51.165691</td>\n",
" <td>10.451526</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>...</td>\n", " <td>...</td>\n",
" <td>2405263</td>\n", " <td>1068960</td>\n",
" <td>2416037</td>\n", " <td>1073971</td>\n",
" <td>2427069</td>\n", " <td>1079084</td>\n",
" <td>2436506</td>\n", " <td>1084021</td>\n",
" <td>2444177</td>\n", " <td>1088690</td>\n",
" <td>2450295</td>\n", " <td>1092452</td>\n",
" <td>2455569</td>\n", " <td>1096433</td>\n",
" <td>2462061</td>\n", " <td>1101430</td>\n",
" <td>2472913</td>\n", " <td>1105544</td>\n",
" <td>2484306</td>\n", " <td>111703914</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3</th>\n", " <th>Iran</th>\n",
" <td>NaN</td>\n", " <td>0</td>\n",
" <td>Iran</td>\n", " <td>0</td>\n",
" <td>32.427908</td>\n", " <td>0</td>\n",
" <td>53.688046</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",
...@@ -566,7 +662,6 @@ ...@@ -566,7 +662,6 @@
" <td>0</td>\n", " <td>0</td>\n",
" <td>0</td>\n", " <td>0</td>\n",
" <td>...</td>\n", " <td>...</td>\n",
" <td>1590605</td>\n",
" <td>1598875</td>\n", " <td>1598875</td>\n",
" <td>1607081</td>\n", " <td>1607081</td>\n",
" <td>1615184</td>\n", " <td>1615184</td>\n",
...@@ -576,141 +671,69 @@ ...@@ -576,141 +671,69 @@
" <td>1648174</td>\n", " <td>1648174</td>\n",
" <td>1656699</td>\n", " <td>1656699</td>\n",
" <td>1665103</td>\n", " <td>1665103</td>\n",
" <td>208249840</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>4</th>\n", " <th>Germany</th>\n",
" <td>NaN</td>\n",
" <td>Italy</td>\n",
" <td>41.871940</td>\n",
" <td>12.567380</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>0</td>\n",
" <td>...</td>\n", " <td>1</td>\n",
" <td>2832162</td>\n",
" <td>2848564</td>\n",
" <td>2868435</td>\n",
" <td>2888923</td>\n",
" <td>2907825</td>\n",
" <td>2925265</td>\n",
" <td>2938371</td>\n",
" <td>2955434</td>\n",
" <td>2976274</td>\n",
" <td>2999119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>Japan</td>\n",
" <td>36.204824</td>\n",
" <td>138.252924</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n", " <td>4</td>\n",
" <td>4</td>\n", " <td>4</td>\n",
" <td>...</td>\n",
" <td>426828</td>\n",
" <td>427732</td>\n",
" <td>428816</td>\n",
" <td>429873</td>\n",
" <td>431093</td>\n",
" <td>432090</td>\n",
" <td>432778</td>\n",
" <td>433700</td>\n",
" <td>434944</td>\n",
" <td>436093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>Korea, South</td>\n",
" <td>35.907757</td>\n",
" <td>127.766922</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n", " <td>4</td>\n",
" <td>5</td>\n",
" <td>...</td>\n", " <td>...</td>\n",
" <td>88120</td>\n", " <td>2416037</td>\n",
" <td>88516</td>\n", " <td>2427069</td>\n",
" <td>88922</td>\n", " <td>2436506</td>\n",
" <td>89321</td>\n", " <td>2444177</td>\n",
" <td>89676</td>\n", " <td>2450295</td>\n",
" <td>90031</td>\n", " <td>2455569</td>\n",
" <td>90372</td>\n", " <td>2462061</td>\n",
" <td>90816</td>\n", " <td>2472913</td>\n",
" <td>91240</td>\n", " <td>2484306</td>\n",
" <td>91638</td>\n", " <td>255393641</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>7</th>\n", " <th>Italy</th>\n",
" <td>NaN</td>\n", " <td>0</td>\n",
" <td>Netherlands</td>\n", " <td>0</td>\n",
" <td>52.132600</td>\n", " <td>0</td>\n",
" <td>5.291300</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>0</td>\n", " <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n", " <td>...</td>\n",
" <td>1064598</td>\n", " <td>2848564</td>\n",
" <td>1068960</td>\n", " <td>2868435</td>\n",
" <td>1073971</td>\n", " <td>2888923</td>\n",
" <td>1079084</td>\n", " <td>2907825</td>\n",
" <td>1084021</td>\n", " <td>2925265</td>\n",
" <td>1088690</td>\n", " <td>2938371</td>\n",
" <td>1092452</td>\n", " <td>2955434</td>\n",
" <td>1096433</td>\n", " <td>2976274</td>\n",
" <td>1101430</td>\n", " <td>2999119</td>\n",
" <td>1105544</td>\n", " <td>312517063</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>8</th>\n", " <th>Spain</th>\n",
" <td>NaN</td>\n",
" <td>Portugal</td>\n",
" <td>39.399900</td>\n",
" <td>-8.224500</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>0</td>\n", " <td>0</td>\n",
" <td>...</td>\n",
" <td>799106</td>\n",
" <td>800586</td>\n",
" <td>801746</td>\n",
" <td>802773</td>\n",
" <td>803844</td>\n",
" <td>804562</td>\n",
" <td>804956</td>\n",
" <td>805647</td>\n",
" <td>806626</td>\n",
" <td>807456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>Spain</td>\n",
" <td>40.463667</td>\n",
" <td>-3.749220</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>...</td>\n", " <td>...</td>\n",
" <td>3161432</td>\n",
" <td>3170644</td>\n", " <td>3170644</td>\n",
" <td>3180212</td>\n", " <td>3180212</td>\n",
" <td>3188553</td>\n", " <td>3188553</td>\n",
...@@ -720,21 +743,45 @@ ...@@ -720,21 +743,45 @@
" <td>3130184</td>\n", " <td>3130184</td>\n",
" <td>3136321</td>\n", " <td>3136321</td>\n",
" <td>3142358</td>\n", " <td>3142358</td>\n",
" <td>355880364</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>10</th>\n", " <th>France</th>\n",
" <td>NaN</td>\n", " <td>0</td>\n",
" <td>United Kingdom</td>\n", " <td>0</td>\n",
" <td>55.378100</td>\n", " <td>2</td>\n",
" <td>-3.436000</td>\n", " <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>3639501</td>\n",
" <td>3664050</td>\n",
" <td>3689034</td>\n",
" <td>3712474</td>\n",
" <td>3732426</td>\n",
" <td>3736390</td>\n",
" <td>3759247</td>\n",
" <td>3785326</td>\n",
" <td>3810605</td>\n",
" <td>404492379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Kingdom</th>\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>0</td>\n", " <td>0</td>\n",
" <td>0</td>\n", " <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n", " <td>...</td>\n",
" <td>4134639</td>\n",
" <td>4144577</td>\n", " <td>4144577</td>\n",
" <td>4154562</td>\n", " <td>4154562</td>\n",
" <td>4163085</td>\n", " <td>4163085</td>\n",
...@@ -744,21 +791,21 @@ ...@@ -744,21 +791,21 @@
" <td>4188400</td>\n", " <td>4188400</td>\n",
" <td>4194785</td>\n", " <td>4194785</td>\n",
" <td>4201358</td>\n", " <td>4201358</td>\n",
" <td>404528634</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>11</th>\n", " <th>US</th>\n",
" <td>NaN</td>\n",
" <td>US</td>\n",
" <td>40.000000</td>\n",
" <td>-100.000000</td>\n",
" <td>1</td>\n", " <td>1</td>\n",
" <td>1</td>\n", " <td>1</td>\n",
" <td>2</td>\n", " <td>2</td>\n",
" <td>2</td>\n", " <td>2</td>\n",
" <td>5</td>\n", " <td>5</td>\n",
" <td>5</td>\n", " <td>5</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n", " <td>...</td>\n",
" <td>28234656</td>\n",
" <td>28309085</td>\n", " <td>28309085</td>\n",
" <td>28386492</td>\n", " <td>28386492</td>\n",
" <td>28463190</td>\n", " <td>28463190</td>\n",
...@@ -768,134 +815,115 @@ ...@@ -768,134 +815,115 @@
" <td>28694071</td>\n", " <td>28694071</td>\n",
" <td>28759980</td>\n", " <td>28759980</td>\n",
" <td>28827144</td>\n", " <td>28827144</td>\n",
" </tr>\n", " <td>3343145027</td>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Hong Kong</td>\n",
" <td>China</td>\n",
" <td>22.300000</td>\n",
" <td>114.200000</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>10896</td>\n",
" <td>10913</td>\n",
" <td>10926</td>\n",
" <td>10950</td>\n",
" <td>10983</td>\n",
" <td>11005</td>\n",
" <td>11019</td>\n",
" <td>11032</td>\n",
" <td>11046</td>\n",
" <td>11055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>NaN</td>\n",
" <td>China</td>\n",
" <td>40.182400</td>\n",
" <td>116.414200</td>\n",
" <td>548</td>\n",
" <td>641</td>\n",
" <td>918</td>\n",
" <td>1401</td>\n",
" <td>2067</td>\n",
" <td>2869</td>\n",
" <td>...</td>\n",
" <td>89911</td>\n",
" <td>89919</td>\n",
" <td>89925</td>\n",
" <td>89935</td>\n",
" <td>89941</td>\n",
" <td>89960</td>\n",
" <td>89971</td>\n",
" <td>89981</td>\n",
" <td>89991</td>\n",
" <td>90000</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"<p>14 rows × 412 columns</p>\n", "<p>14 rows × 409 columns</p>\n",
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n",
"0 NaN Belgium 50.833300 4.469936 0 0 \n", "Localisation \n",
"1 NaN France 46.227600 2.213700 0 0 \n", "Hong-Kong 0 2 2 5 8 8 8 \n",
"2 NaN Germany 51.165691 10.451526 0 0 \n", "Korea, South 1 1 2 2 3 4 4 \n",
"3 NaN Iran 32.427908 53.688046 0 0 \n", "China 548 641 918 1401 2067 2869 5501 \n",
"4 NaN Italy 41.871940 12.567380 0 0 \n", "Japan 2 2 2 2 4 4 7 \n",
"5 NaN Japan 36.204824 138.252924 2 2 \n", "Portugal 0 0 0 0 0 0 0 \n",
"6 NaN Korea, South 35.907757 127.766922 1 1 \n", "Belgium 0 0 0 0 0 0 0 \n",
"7 NaN Netherlands 52.132600 5.291300 0 0 \n", "Netherlands 0 0 0 0 0 0 0 \n",
"8 NaN Portugal 39.399900 -8.224500 0 0 \n", "Iran 0 0 0 0 0 0 0 \n",
"9 NaN Spain 40.463667 -3.749220 0 0 \n", "Germany 0 0 0 0 0 1 4 \n",
"10 NaN United Kingdom 55.378100 -3.436000 0 0 \n", "Italy 0 0 0 0 0 0 0 \n",
"11 NaN US 40.000000 -100.000000 1 1 \n", "Spain 0 0 0 0 0 0 0 \n",
"12 Hong Kong China 22.300000 114.200000 0 2 \n", "France 0 0 2 3 3 3 4 \n",
"13 NaN China 40.182400 116.414200 548 641 \n", "United Kingdom 0 0 0 0 0 0 0 \n",
"US 1 1 2 2 5 5 5 \n",
"\n", "\n",
" 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/23/21 2/24/21 \\\n", " 1/29/20 1/30/20 1/31/20 ... 2/24/21 2/25/21 \\\n",
"0 0 0 0 0 ... 757696 760809 \n", "Localisation ... \n",
"1 2 3 3 3 ... 3608271 3639501 \n", "Hong-Kong 10 10 12 ... 10913 10926 \n",
"2 0 0 0 1 ... 2405263 2416037 \n", "Korea, South 4 4 11 ... 88516 88922 \n",
"3 0 0 0 0 ... 1590605 1598875 \n", "China 6077 8131 9790 ... 89919 89925 \n",
"4 0 0 0 0 ... 2832162 2848564 \n", "Japan 7 11 15 ... 427732 428816 \n",
"5 2 2 4 4 ... 426828 427732 \n", "Portugal 0 0 0 ... 800586 801746 \n",
"6 2 2 3 4 ... 88120 88516 \n", "Belgium 0 0 0 ... 760809 763885 \n",
"7 0 0 0 0 ... 1064598 1068960 \n", "Netherlands 0 0 0 ... 1068960 1073971 \n",
"8 0 0 0 0 ... 799106 800586 \n", "Iran 0 0 0 ... 1598875 1607081 \n",
"9 0 0 0 0 ... 3161432 3170644 \n", "Germany 4 4 5 ... 2416037 2427069 \n",
"10 0 0 0 0 ... 4134639 4144577 \n", "Italy 0 0 2 ... 2848564 2868435 \n",
"11 2 2 5 5 ... 28234656 28309085 \n", "Spain 0 0 0 ... 3170644 3180212 \n",
"12 2 5 8 8 ... 10896 10913 \n", "France 5 5 5 ... 3639501 3664050 \n",
"13 918 1401 2067 2869 ... 89911 89919 \n", "United Kingdom 0 0 2 ... 4144577 4154562 \n",
"US 6 6 8 ... 28309085 28386492 \n",
"\n", "\n",
" 2/25/21 2/26/21 2/27/21 2/28/21 3/1/21 3/2/21 3/3/21 \\\n", " 2/26/21 2/27/21 2/28/21 3/1/21 3/2/21 3/3/21 \\\n",
"0 763885 766654 769414 771511 772294 774344 777608 \n", "Localisation \n",
"1 3664050 3689034 3712474 3732426 3736390 3759247 3785326 \n", "Hong-Kong 10950 10983 11005 11019 11032 11046 \n",
"2 2427069 2436506 2444177 2450295 2455569 2462061 2472913 \n", "Korea, South 89321 89676 90031 90372 90816 91240 \n",
"3 1607081 1615184 1623159 1631169 1639679 1648174 1656699 \n", "China 89935 89941 89960 89971 89981 89991 \n",
"4 2868435 2888923 2907825 2925265 2938371 2955434 2976274 \n", "Japan 429873 431093 432090 432778 433700 434944 \n",
"5 428816 429873 431093 432090 432778 433700 434944 \n", "Portugal 802773 803844 804562 804956 805647 806626 \n",
"6 88922 89321 89676 90031 90372 90816 91240 \n", "Belgium 766654 769414 771511 772294 774344 777608 \n",
"7 1073971 1079084 1084021 1088690 1092452 1096433 1101430 \n", "Netherlands 1079084 1084021 1088690 1092452 1096433 1101430 \n",
"8 801746 802773 803844 804562 804956 805647 806626 \n", "Iran 1615184 1623159 1631169 1639679 1648174 1656699 \n",
"9 3180212 3188553 3188553 3188553 3204531 3130184 3136321 \n", "Germany 2436506 2444177 2450295 2455569 2462061 2472913 \n",
"10 4154562 4163085 4170519 4176554 4182009 4188400 4194785 \n", "Italy 2888923 2907825 2925265 2938371 2955434 2976274 \n",
"11 28386492 28463190 28527344 28578548 28637313 28694071 28759980 \n", "Spain 3188553 3188553 3188553 3204531 3130184 3136321 \n",
"12 10926 10950 10983 11005 11019 11032 11046 \n", "France 3689034 3712474 3732426 3736390 3759247 3785326 \n",
"13 89925 89935 89941 89960 89971 89981 89991 \n", "United Kingdom 4163085 4170519 4176554 4182009 4188400 4194785 \n",
"US 28463190 28527344 28578548 28637313 28694071 28759980 \n",
"\n", "\n",
" 3/4/21 \n", " 3/4/21 Sum \n",
"0 780251 \n", "Localisation \n",
"1 3810605 \n", "Hong-Kong 11055 1659701 \n",
"2 2484306 \n", "Korea, South 91638 10964482 \n",
"3 1665103 \n", "China 90000 33057311 \n",
"4 2999119 \n", "Japan 436093 41743265 \n",
"5 436093 \n", "Portugal 807456 70635994 \n",
"6 91638 \n", "Belgium 780251 98653796 \n",
"7 1105544 \n", "Netherlands 1105544 111703914 \n",
"8 807456 \n", "Iran 1665103 208249840 \n",
"9 3142358 \n", "Germany 2484306 255393641 \n",
"10 4201358 \n", "Italy 2999119 312517063 \n",
"11 28827144 \n", "Spain 3142358 355880364 \n",
"12 11055 \n", "France 3810605 404492379 \n",
"13 90000 \n", "United Kingdom 4201358 404528634 \n",
"US 28827144 3343145027 \n",
"\n", "\n",
"[14 rows x 412 columns]" "[14 rows x 409 columns]"
] ]
}, },
"execution_count": 6, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"data_list = []\n",
"# récupération des données pour tous les pays sauf la Chine\n",
"for country in countries:\n",
" data_list.append(raw_data[(raw_data['Province/State'].isnull()) & (raw_data['Country/Region']==country)].values.tolist()[0])\n",
"# récupération des données pour Hong-Kong\n",
"data_hk = raw_data[(raw_data['Province/State'] == \"Hong Kong\") & (raw_data['Country/Region']== \"China\")].values.tolist()[0]\n",
"data_hk[0] = pd.np.NaN\n",
"data_hk[1] = \"Hong-Kong\"\n",
"data_list.append(data_hk)\n",
"# récupération des données pour le reste de la Chine en sommant les différentes colonnes\n",
"data_china = raw_data[(raw_data['Province/State'] != \"Hong Kong\") & (raw_data['Country/Region']== \"China\")].sum().values.tolist()\n",
"# mise à jour des premières colonnes des données de la Chine et ajout à la liste\n",
"data_china[0] = pd.np.NaN\n",
"data_china[1] = \"China\"\n",
"data_list.append(data_china)\n",
"# création du nouveau tableau avec toutes les données des pays recherchés\n",
"data = pd.DataFrame(data_list, columns=raw_data.columns)\n",
"# suppression des colonnes inutilisées\n",
"data = data.drop(columns=['Province/State', 'Lat', 'Long'])\n",
"# chaangement de l'index\n",
"data = data.rename(columns={\"Country/Region\" : \"Localisation\"}).set_index(\"Localisation\")\n",
"# création d'une colonne correspondant à la somme des cas et tri en fonction de cette colonne\n",
"data[\"Sum\"] = data.sum(axis=1)\n",
"data = data.sort_values(by ='Sum')\n",
"data" "data"
] ]
}, },
...@@ -908,10 +936,75 @@ ...@@ -908,10 +936,75 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"data": {
"image/png": 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HYZ0rcrv+DqwoqZSy4C8R8R4wA+hHa6aDGbRxLJz1wMysPjoKdu2O8p/Nz88LqO4a4GLAlrmnNzQiVo2IuXlZpeBa7rv5+fx26swvTKvsuS2V9t3WOqW68wEiYiHwXg6UAAvxzzrMzBpGu8GuvVH+O7GPucCyhfnbaA1WSBqaJ+8CDsplX+SjQbZoIfAVYD1JJ3WiLfcA++Vrd6uQbp4BeBRYU9Jaef4rhXWK7RoBzI6INzqxTzMzq7NqrtkdAvw0n3b8O3mU/07s42Zgn9INKsDRwPB8w8fDpBtYIF0b207Sg8AuwHPtbTQi5pOule0p6TtVtuV64HlgJnARMAGYExHvAEcCf843qDxbWGdUqb2ka38jq9yXmZk1CLWeeesbJA2MiHk5sepEYOt8/a6mBgxZJ4aMPLPWu62KR1Axs0YlaXJEDO/sen3xutJYScsDSwAn1yPQgbMemJnVUp8LdhExot5tMDOz2vLYmGZm1vT6XM+uUTjrgZlZ7bhnZ2ZmTa9pe3aSFpBGMinZOw9fZmZmfUzTBjvg7YgYWmmhpMUj4v1aNsjMzOqjT53GzJkRrpV0M3CbpIGS7pD0YM5WsFeu1yLpEUmX5AwJt0laKi9bW9Ltkqbl9dbK5T/KWRymSzqxji/TzMzKNHOwWyqP2jJV0g2F8i2BkRGxI/AOsE9EDAN2AH4tqTQW5jrAeRHxGeB1UoYGgCtz+cbAVqTh1HbJ9TcHhgKbStqup1+gmZlVpy+exvxbRLyapwX8PAemhcCqtGZIeCYipubpyUBLzs6wakTcAJCHGSMHu11ICWQBBpKC313FHUs6kjQsGf2WW6nrr9DMzKrSzMGukjcL0wcBKwGbRsR7kmYBS+ZlxawJC4ClqJw1QcCpEXFRezuOiIuBiyENF9b5ppuZ2aJo5tOY1RgEvJID3Q7AGu1VztkOnpe0N4CkAZKWBv4KfEPSwFy+qqSVe7jtZmZWpb4e7K4kZTSYROrlPVrFOgcDR+csCPcBH4+I24CrgPslzQCu48NpjczMrI76XNaDRuGsB2ZmneesB72Msx6YmdVOXz+NaWZmfYCDnZmZNT2fxqyTRsl64OtzZtYXuGdnZmZNz8HOzMyaXp8MdpJ+kgd4np7HzvzcImxjT0nH9UT7zMyse/W5a3aStgR2B4ZFxHxJg4ElOrudiLgJuKm722dmZt2vL/bshgCzI2I+QETMjogXJc2SdLqkifmxNoCkPSRNkDQlp/ZZJZcfKuncPD1a0tmS7pP0tKT96/bqzMzsI/pisLsN+KSkxyWdL2n7wrI3ImJz4FygNLzJPcAWEbEJcDXw4wrbHQJsQ+o1ntZWBUlHSpokadKCt+Z0x2sxM7Mq9LnTmBExT9KmwLakHHbXFK69/aHwfEaeXi3XGUI63flMhU3fGBELgYdLvb829u2sB2ZmddAXe3ZExIKIGBcRJwDfpTUxazEAlabPAc6NiA2Bb9KaAqhcMSVQpVRAZmZWB30u2ElaT9I6haKhwLN5+oDC8/15ehDwQp4e2fMtNDOz7tbnTmOSsoifI2l54H3gSVL28N2BAZImkL4EfCXXHwVcK+kFYDywZs1bbGZmXeIUP1nOUj48ImbXYn+NkuLHw4WZWW/iFD+9jFP8mJnVjoNdFhEt9W6DmZn1jD53g4qZmfU97tnVSb1S/PganZn1Re7ZmZlZ03OwK5A0Lz+3SPpqFfVbJM3s+ZaZmVlXONi1rQXoMNiZmVnv4GDXttOAbXOuu2NyD+5uSQ/mx1blK+TlQwvz90raqKatNjOzNjnYte044O6IGBoRZwCvADtHxDDSUGJnt7HOpcChAJLWBQZExPRiBWc9MDOrDwe76vQHLpE0A7gW2KCNOtcCu0vqD3wDGF1eISIujojhETG839KDerK9ZmZW4J8eVOcY4GVgY9IXhHfKK0TEW5L+BuwFfBno9HA2ZmbWMxzs2jYXWLYwPwh4PiIWShoJ9Kuw3qXAzaRToK/2cBvNzKxKPo3ZtunA+5KmSToGOB8YKWk8sC7wZlsrRcRk4A3gdzVrqZmZdcg9u4KIGJif3wN2KltcvLPyv3O9WcBnS4WSPkH6AnFbjzbUzMw6xcGum0g6BDgF+GFELOyovrMemJnVjoNdN4mIMcCYerfDzMw+ytfszMys6blnVye1yHrgDAdmZol7dmZm1vR6VbCTtIqkqyQ9LWmypPsl7VPvdpmZWWPrNcFOkoAbgbsi4lMRsSlwILBaletX+iG4mZk1uV4T7IAdgXcj4sJSQUQ8GxHnSOon6ZeSHpA0XdI3ASSNkHSnpKuAGTl7waOSLpU0U9KVkj6fMxQ8IWnzvN7mku6TNCU/r5fLD5X0J0m35vq/yOWHSTqj1C5JR0j6TS0PjpmZVdabgt1ngAcrLDsMmBMRmwGbAUdIWjMv2xz4SUSUBm9eGziL9CPx9Ul567YBjgWOz3UeBbaLiE2AnwE/L+xrKCnzwYbAAZI+CVwN7JkHgQb4Oh5FxcysYfTauzElnUcKUu8CzwIbSdo/Lx4ErJOXTYyIZwqrPhMRM/I2HgLuiIjIGQ1aCutfLmkdIEhZD0ruiIg5ef2HgTUi4h+S/k7KevAI0L+0j7I2HwkcCdBvuZW6fAzMzKw6valn9xAwrDQTEd8hDem1EiDgezn/3NCIWDMiSkN2lY9jOb8wvbAwv5DW4H8ycGdEfBbYA1iywvoLCuuU8tlV7NU5xY+ZWX30pmD3d2BJSd8qlC2dn/8KfKt0GlHSupKW6cK+BgEv5OlDq1khIiYAnySdFv1DF/ZtZmbdrNcEu4gIYG9ge0nPSJoIXA78F6lX9TDwoKSZwEV07RTtL4BTJd1L5XQ+bfkjcG9EvNaFfZuZWTdTiiHWHSSNBc6IiDs6qjtgyDoxZOSZPdoej6BiZs1G0uSI6HRy7F57g0ojkbQ8MBGYVk2gA2c9MDOrJQe7bhARr5OSupqZWQPqNdfszMzMFpV7dnXS3VkPfH3OzKwy9+zMzKzpOdiZmVnTc7Brg6R59W6DmZl1Hwe7KjlFkJlZ7+Vg147yFEG57MacOPahPLBzqe48SadImiZpvKRV6tZwMzP7EAe7jpWnCPpGThw7HDha0oq5fBlgfERsDNwFHFG+IUlHSpokadKCt+bUou1mZoaDXTXKUwQdLWkaMJ408PM6ufxdYGyenkxruqAPOOuBmVl9+Hd2HfsgRZCkEcDngS0j4i1J42hN//NetA40Wkz9Y2ZmdeaeXecMAl7LgW59YIt6N8jMzDrmYNc5twKLS5pOSvA6vs7tMTOzKvhUWxsiYmB+HgeMK5TPB77Y3jp5+jrguh5tpJmZVc3Brk6c4sfMrHZ8GtPMzJqeg52ZmTU9n8ask86k+HH6HjOzrnHPzszMml5Ng52kkPTrwvyxkkZ1sM4ISVsV5kdL2r+L7ZglaXBXtlHYljMkmJk1uFr37OYD+3Yy0IwAtuqoUjWUuDdrZtbH1PqD/33gYuCY8gWSVpJ0vaQH8mNrSS3AUcAxkqZK2jZX307SfZKeLvbyJP0orztd0om5rEXSI5LOBx4kjWdZ3G+nshhIWlPS/Xk/JxfqD5F0V27nzEJbzcyszurRyzkPOEhS+UjIZwFnRMRmwH7ApRExC7gwlw+NiLtz3SHANsDuwGkAknYhDcq8OTAU2FTSdrn+esCYiNgkIp4t229nsxicBVyQ2/nPwna+Cvw1IoYCGwNTy1+4sx6YmdVHze/GjIg3JI0BjgbeLiz6PLCBpNL8cpKWrbCZGyNiIfBwIW/cLvkxJc8PJAW/54BnI6LS0F5HS9onT5eyGPybj2Yx2DlPb00KxgBXAKfn6QeAyyT1z+37SLCLiItJPVsGDFknypebmVnPqNdPD84knVL8XaFsMVI2gWIApBD8iuYXqxSeT42Ii8rWb6GQuaBs2QgWLYvBRwJVRNyVe5K7AVdI+mVEjGlrv2ZmVlt1uVkjIl4F/ggcVii+DfhuaUbS0Dw5F6jUwyv6K/ANSQPz+qtKWrmDdRYli8G9wIF5+qBCe9cAXomIS4DfAsOq2JaZmdVAPe9M/DVQvCvzaGB4vrnkYdKNKQA3A/uU3aDyERFxG3AVcL+kGaSBmDsKkouSxeD7wHckPUAKliUjgKmSppBOc55VxbbMzKwG1HqmzmppwJB1YsjIM6uq6xFUzMwSSZMjYnhn1/NwYXXirAdmZrXjH1ibmVnTc7AzM7Om59OYdVJt1gNfrzMz6zr37MzMrOn16mAnaUH+ScI0SQ8WsyO0s06HWQokXSppg+5ppZmZ1VtvP435dh6LEklfAE4Ftu/qRiPi8K5uw8zMGkev7tmVWQ54rTTTVgaEIkmLSTo/ZzsYK+mWUgYFSeMkDc/T8wrr7C9pdJ4eLekCSXfm7AvbS7osZ1gY3cOv1czMOqG39+yWkjSVNJ7lEGBH+EgGBAE3SdouIu4qrLsv0AJsCKwMPAJc1sn9r5D3uSdppJetgcOBByQNbWswaDMzq73e3rN7O6f+WR/YFRijNHJ0MQPCg8D6pOBXtA1wbUQsjIh/Ancuwv5vzoNFzwBejogZORvDQ6RA+iFO8WNmVh+9vWf3gYi4P2dAX4kKGRDKtJlOoa1NF6aXLFtWyr6wkA9nYlhIG8fWKX7MzOqjt/fsPpCzFvQj5aKrJgPCPcB++drdKqSBnNvysqRPS1oM2KdCHTMza2C9vWdXumYHqac2MiIWALdJ+jQpAwLAPOBrwCuFda8HdgJmAo8DE4C2zi0eR0ri+o9cd2APvA4zM+tBfTrrgaSBETFP0orARGDrfP2ux1Wb9cAjqJiZtXLWg0UzVtLywBLAybUKdOCsB2ZmtdSng11EjKh3G8zMrOc1zQ0qZmZmlfTpnl09tZf1wNfpzMy6l3t2ZmbW9BzszMys6fXqYFdI8TNT0rWSlu7k+sf3ULtaJM3siW2bmVnn9epgR+vYmJ8F3gWOqmYlJYsBPRLszMyssfT2YFd0N7A2gKQf5t7eTEk/yGUtOf3O+aTBoX9LHoFF0pXlvTFJx0oalac3y6mC7pf0y1K9vM7dOXFsVcljzcys9poi2ElaHPgiMEPSpsDXgc8BWwBHSNokV10PGBMRm0TE12ntGR7UwS5+BxwVEVsCCwrlrwA7R8Qw4ADg7A7a6awHZmZ10NuDXWlszEnAc6Te2jbADRHxZkTMA/4EbJvrPxsR4zuzgzzCyrIRcV8uuqqwuD9wiaQZwLXABu1tKyIujojhETG839KDOtMMMzPrgt7+O7u3I2JosSDns6vkzXaWvc+Hg38pnU972zsGeBnYOK/7Tjt1zcysTnp7z64tdwF7S1pa0jKktDx3V6j7nqT+efplYGVJK0oaAOwOEBGvAXMlbZHrHVhYfxDwUk7YejApxZCZmTWYpgt2EfEgMJqUxWACcGlETKlQ/WJguqQrI+I94KS8zljg0UK9w4CLJd1P6umVLridD4yUNB5Yl/Z7jmZmVid9OsVPtUqpgPL0ccCQiPh+V7bZXoofDxdmZtY2p/jpWbtJ+m/S8XoWOLSrG3SKHzOz2nGwq0JEXANcU+92mJnZonGwq5PyrAc+dWlm1nOa7gYVMzOzcg52ZmbW9PpksJM0r95tMDOz2umTwc7MzPqWPhvsJA2UdEfOVjBD0l65vEXSo5Iuz5kOrivlyZP0M0kP5GwKF5eGJpM0TtLpkiZKelzStu3t28zMaqvPBjvSOJb75IwFOwC/LoyruR5wcURsBLwBfDuXnxsRm+X8eUuRhxTLFo+IzYEfACe0tUNnPTAzq4++HOwE/FzSdOB2YFVglbzsHxFxb57+PSmTAsAOkibkLAc7Ap8pbO9P+Xky0NLWDp31wMysPvry7+wOAlYCNo2I9yTNojXTQfkYaiFpSdJYmMMj4h85seuShTrz8/MC+vZxNTNrOH25ZzcIeCUHuh2ANQrLVpe0ZZ7+CnAPrYFttqSBwP61a6qZmXVFnwt2Oav5fOBKYLikSaReXjHLwSOkbAbTgY8BF0TE68AlwAzgRuCBmjbczMwWWV883fYZ4KmImA1sWb5QUguwMCKOKl8WET8FftpG+YjC9GwqXLMzM7P66FPBTtJRwNGkOybrylkPzMxqp08Fu4i4ELiwgzqzgM/sCynpAAAND0lEQVTWpEFmZlYTfe6anZmZ9T19qmfXSJzix8ysdtyzMzOzptfngp2kj0u6WtJTkh6WdEsexmtshfqXStqg1u00M7Pu06dOY+axL28ALo+IA3PZUGCPSutExOE1ap6ZmfWQvtaz2wF4L9+VCUBETAXuBgbmDAePSrqyLKPB8Dw9T9IpkqZJGi9plVy+Rx4zc4qk20vlZmbWGPpasPssaaDmtmxC+v3dBsCngK3bqLMMMD4iNgbuAo7I5fcAW0TEJsDVwI+7s9FmZtY1feo0ZgcmRsTzAJKmkkZBuaeszrtA6dreZGDnPL0acI2kIcASwDNt7UDSkcCRAP2WW6k7225mZu3oaz27h4BNKyybX5iulLngvYiINuqcQ8p1tyHwTT6cDeEDTvFjZlYffS3Y/R0YIKl0+hFJmwHbd3G7g4AX8vTILm7LzMy6WZ8KdrlXtg+wc/7pwUPAKODFLm56FHCtpLuB2V3clpmZdTO1npWzWhowZJ0YMvLMD+Y9goqZWcckTY6I4Z1dzzeo1ImzHpiZ1U6fOo1pZmZ9k4OdmZk1PQe7Opnxwpx6N8HMrM9wsDMzs6bnYGdmZk2vx4OdpHmF6S9JekLS6j293wpt+YakGZKmS5opaa9F3M5QSV8qzI+SdGz3tdTMzLpTzX56IGkn0rBau0TEc1Wus3hEvN9N+18N+AkwLCLmSBoILOoAlUOB4cAt3dE2MzPrWTU5jSlpW+ASYLeIeCqXrSHpjtzLuqPU25M0WtJvJN0JnC5pGUmXSXogp9DZK9drkXS3pAfzY6sOmrEyMBeYBxAR8yLimbytoTllz3RJN0haIZcX0/sMljRL0hLAScABkqZKOiBvf4Nc/2lJR3fj4TMzsy6qRbAbAPwvsHdEPFooPxcYExEbAVcCZxeWrQt8PiL+k9Qb+3tEbEbKR/dLScsArwA7R8Qw4ICy9dsyDXgZeEbS7yQVE7aOAf4rt2UGcEKljUTEu8DPgGsiYmhEXJMXrQ98AdgcOEFS//J1c0b0SZImLXjLd2OamdVKLYLde8B9wGFl5VsCV+XpK4BtCsuujYgFeXoX4LicdmccKaPA6kB/4BJJM4BrSXnoKsrb2xXYH3gcOCNfaxsELB8R/5erXg5s19kXCfw5IuZHxGxSIP5IAldnPTAzq49aBLuFwJeBzSQd30694iCdbxamBeyXe1FDI2L1iHgEOIbUU9uYdP1siY4aEsnEiDgVOBDYr4NV3qf1GLWZtqegmhRBZmZWBzW5ZhcRbwG7AwdJKvXw7iMFHICD+Gii1JK/At+TJABJm+TyQcBLEbEQOBjoV1pB0qPlG5H0CUnDCkVDgWcjYg7wWr6uSN5WqZc3i9b8d/sX1p0LLFvxBZuZWUOpWe8jIl6VtCtwl6TZwNHAZZJ+BPwL+HqFVU8GzgSm54A3ixQ4zweul/QfwJ3k3qCkwaTeYLn+wK8kfQJ4J+/zqLxsJHChpKWBpwtt+RXwR0kHk3LhldxJ66nVUzt1IMzMrOaaLsWPpN2BT0VERzes1NWAIevE/JeeqHczzMx6Faf4ySJibL3bUI0NV/UNKmZmteLhwszMrOk52NWJsx6YmdWOg52ZmTU9BzszM2t6DRHsipkR8vyhks7t4X22SJpZmD8ij7G5Qk/u18zMaq/p7sZcFPl3dN8DdoyI1+rdHjMz614N0bNrTwfZEc6WdF/ONLB/Ll9M0vmSHpI0VtItpWUVtv9l4DhS6qHZuay9LAinS5oo6fHSqCuSlpb0x1z/GkkTStkSzMys/hol2C2V0+VMzaOSnFRY1l52hCGkAaR3B07LZfsCLcCGwOGkAacrWSNvf5eI+GehvL0sCItHxObADwrl3wZey/VPpnWIsQ9x1gMzs/polGD3dmGg56GkFDol7WVHuDEiFkbEw7RmGdiGlDVhYQ5gd7az338Bz5EGqgagiiwIf8rPk0lBtbTPqwEiYiYwva2dOeuBmVl9NEqw64zi+GbFTAMqe/4QSZ8r9B73zMVvAV8EjpJ0UJX7L+2zmNmgzX2amVlj6A3BrtrsCCX3APvla3erACMAImJCofd4U6lyRPyLlOfu55K+0EEWhPb2+WUASRuQTqGamVmD6A13Y1abHaHkemAnYCYpSesEoN0LZBHxTO7t3SJpXypnQajkfOBySdOBKaTTmL4oZ2bWIJou6wGApIERMU/SisBEYOuyG1C6e3/9gP4R8Y6ktYA7gHUj4t1K6zjrgZlZ5znrwYeNlbQ8KXv5yT0Z6LKlgTsl9Sddv/tWe4EOnPXAzKyWmjLYRcSIGu9vLuDf1ZmZNajecIOKmZlZlzjYmZlZ03OwMzOzpudgZ2ZmTc/BzszMmp6DnZmZNT0HOzMza3oOdmZm1vSacriw3kDSXOCxerejCwYDs+vdiC5w++unN7cd3P56GgwsExErdXbFphxBpZd4bFHGd2sUkia5/fXTm9vfm9sObn895ba3LMq6Po1pZmZNz8HOzMyanoNd/Vxc7wZ0kdtfX725/b257eD219Mit903qJiZWdNzz87MzJqeg52ZmTU9B7seJmlXSY9JelLScW0sl6Sz8/LpkobVo52VVNH+EZLmSJqaHz+rRzvbIukySa9ImllheaMf+47a38jH/pOS7pT0iKSHJH2/jToNe/yrbH9DHn9JS0qaKGlabvuJbdRp5GNfTfs7f+wjwo8eegD9gKeATwFLANOADcrqfAn4CyBgC2BCvdvdyfaPAMbWu60V2r8dMAyYWWF5wx77KtvfyMd+CDAsTy8LPN7L/varaX9DHv98PAfm6f7ABGCLXnTsq2l/p4+9e3Y9a3PgyYh4OiLeBa4G9iqrsxcwJpLxwPKShtS6oRVU0/6GFRF3Aa+2U6WRj3017W9YEfFSRDyYp+cCjwCrllVr2ONfZfsbUj6e8/Js//wovxOxkY99Ne3vNAe7nrUq8I/C/PN89B+mmjr1Um3btsynHP4i6TO1aVq3aORjX62GP/aSWoBNSN/Qi3rF8W+n/dCgx19SP0lTgVeAv0VErzr2VbQfOnnsHex6ltooK/+GUk2deqmmbQ8Ca0TExsA5wI093qru08jHvhoNf+wlDQSuB34QEW+UL25jlYY6/h20v2GPf0QsiIihwGrA5pI+W1aloY99Fe3v9LF3sOtZzwOfLMyvBry4CHXqpcO2RcQbpVMOEXEL0F/S4No1sUsa+dh3qNGPvaT+pEBxZUT8qY0qDX38O2p/ox9/gIh4HRgH7Fq2qKGPfUml9i/KsXew61kPAOtIWlPSEsCBwE1ldW4CDsl3R20BzImIl2rd0Ao6bL+kj0tSnt6c9Df175q3dNE08rHvUCMf+9yu3wKPRMRvKlRr2ONfTfsb9fhLWknS8nl6KeDzwKNl1Rr52HfY/kU59s560IMi4n1J3wX+Srqz8bKIeEjSUXn5hcAtpDujngTeAr5er/aWq7L9+wPfkvQ+8DZwYOTbpepN0h9Id20NlvQ8cALpYnfDH3uoqv0Ne+yBrYGDgRn52gvA8cDq0CuOfzXtb9TjPwS4XFI/UhD4Y0SM7S2fO1TX/k4few8XZmZmTc+nMc3MrOk52JmZWdNzsDMzs6bnYGdmZk3Pwc7MzGpCHQxuXlZ3DUl35IGqx0larSv7drAzM7NaGc1Hf+Beya9I43duBJwEnNqVHTvYmZlZTbQ1uLmktSTdKmmypLslrZ8XbQDckafvpIuD0DvYmTU5SYfkU0HTJF0haQ9JEyRNkXS7pFVyve3Vmh9siqRlc/mPJD2Qt3FiLltG0p/zNmdKOqCer9F6tYuB70XEpsCxwPm5fBqwX57eB1hW0oqLuhOPoGLWxPJo8D8Bto6I2ZI+Rhrwd4uICEmHAz8G/pP0QfOdiLg3D4D8jqRdgHVI6Z4E3CRpO2Al4MWI2C3vZ1DNX5z1evnvbCvg2jz6F8CA/HwscK6kQ4G7gBeA9xd1Xw52Zs1tR+C6iJgNEBGvStoQuEYpf9kSwDO57r3AbyRdCfwpIp7PwW4XYEquM5AU/O4GfiXpdFISzbtr95KsiSwGvJ4zHHxIRLwI7AsfBMX9ImJOV3ZkZs1LfDR1yznAuRGxIfBNYEmAiDgNOBxYChifr50IODUihubH2hHx24h4HNgUmAGcKulnNXo91kRy2qRnJP0HpAG4JW2cpwdLKsWo/wYu68q+HOzMmtsdwJdL1zryacxBpFNCACNLFSWtFREzIuJ0YBKwPmkQ8G/kb9ZIWlXSypI+AbwVEb8n3TU3rGavyHqtPLj5/cB6kp6XdBhwEHCYpGnAQ7TeiDICeEzS48AqwCld2rcHgjZrbpJGAj8CFpBOR94AnEEKeOOBzSJihKRzgB1yvYeBQyNivqTvk3p8APOArwFrA78EFgLvAd+KiEm1e1VmneNgZ2ZmTc+nMc3MrOk52JmZWdNzsDMzs6bnYGdmZk3Pwc7MzJqeg52ZmTU9BzszM2t6/x/xhl7OmRx/AwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# récupération des valeurs à afficher\n",
"total = data[\"Sum\"].tolist()\n",
"names = data.index.tolist()\n",
"y_pos = pd.np.arange(len(names))\n",
"\n",
"# création du graphique\n",
"fig, ax = plt.subplots()\n",
"ax.barh(y_pos, total, align='center')\n",
"ax.set_yticks(y_pos)\n",
"ax.set_yticklabels(names)\n",
"ax.set_xlabel('cases')\n",
"ax.set_title(f'Cumulative confirmed cases ({data.columns[-2]})' , fontweight =\"bold\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# création du graphique avec une échèle logarithmique\n",
"fig, ax = plt.subplots()\n",
"ax.barh(y_pos, total, align='center')\n",
"ax.set_yticks(y_pos)\n",
"ax.set_yticklabels(names)\n",
"ax.set_xlabel('cases')\n",
"ax.set_title(f'Cumulative confirmed cases ({data.columns[-2]}) (log scale)' , fontweight =\"bold\")\n",
"plt.xscale(value=\"log\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comme vous pouvez le constater les États Unis comptent beaucoup plus de cas que tous les autres pays, ce qui rend le premier graphe difficile à analyser. Le second nous permet de mieux nuancer les différences sur les autres pays."
]
} }
], ],
"metadata": { "metadata": {
......
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