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| Country_Region | Last_Update | Lat | Long_ | Confirmed | Deaths | Recovered | Active | Incident_Rate | People_Tested | People_Hospitalized | Mortality_Rate | UID | ISO3 |
\n \n 17 | \n US | \n 2020-04-15 12:27:41 | \n 40.000000 | \n -100.000000 | \n 609685 | \n 26059 | \n 49966 | \n 533660 | \n 185.052320 | \n nan | \n nan | \n 4.274174 | \n 840 | \n USA | \n
\n \n 160 | \n Spain | \n 2020-04-15 12:27:22 | \n 40.463667 | \n -3.749220 | \n 177633 | \n 18579 | \n 70853 | \n 88201 | \n 379.924766 | \n nan | \n nan | \n 10.459205 | \n 724 | \n ESP | \n
\n \n 10 | \n Italy | \n 2020-04-15 12:27:22 | \n 41.871900 | \n 12.567400 | \n 162488 | \n 21067 | \n 37130 | \n 104291 | \n 268.744769 | \n nan | \n nan | \n 12.965265 | \n 380 | \n ITA | \n
\n \n 7 | \n Germany | \n 2020-04-15 12:27:22 | \n 51.165700 | \n 10.451500 | \n 132210 | \n 3495 | \n 72600 | \n 56115 | \n 157.798729 | \n nan | \n nan | \n 2.643522 | \n 276 | \n DEU | \n
\n \n 6 | \n France | \n 2020-04-15 12:27:22 | \n 46.227600 | \n 2.213700 | \n 131362 | \n 15750 | \n 29121 | \n 86491 | \n 201.248555 | \n nan | \n nan | \n 11.989769 | \n 250 | \n FRA | \n
\n \n 16 | \n United Kingdom | \n 2020-04-15 12:27:22 | \n 55.000000 | \n -3.000000 | \n 94847 | \n 12131 | \n 344 | \n 82372 | \n 139.715102 | \n nan | \n nan | \n 12.790072 | \n 826 | \n GBR | \n
\n \n 3 | \n China | \n 2020-04-15 10:36:21 | \n 30.592800 | \n 114.305500 | \n 83355 | \n 3346 | \n 78307 | \n 1702 | \n 5.934107 | \n nan | \n nan | \n 4.014156 | \n 156 | \n CHN | \n
\n \n 89 | \n Iran | \n 2020-04-15 12:27:22 | \n 32.427908 | \n 53.688046 | \n 76389 | \n 4777 | \n 49933 | \n 21679 | \n 90.946916 | \n nan | \n nan | \n 6.253518 | \n 364 | \n IRN | \n
\n \n 172 | \n Turkey | \n 2020-04-15 12:27:22 | \n 38.963700 | \n 35.243300 | \n 65111 | \n 1403 | \n 4799 | \n 58909 | \n 77.201471 | \n nan | \n nan | \n 2.154782 | \n 792 | \n TUR | \n
\n \n 32 | \n Belgium | \n 2020-04-15 12:27:22 | \n 50.833300 | \n 4.469936 | \n 33573 | \n 4440 | \n 7107 | \n 22026 | \n 289.681729 | \n nan | \n nan | \n 13.224913 | \n 56 | \n BEL | \n
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"source": [
"# Importer les packages \n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from ipywidgets import interact,widgets\n",
"import folium"
],
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"outputs": []
},
{
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"metadata": {
"id": "Rj7w51bmpu8q"
},
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"# Importer les données\n",
"deces_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n",
"confirme_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n",
"retabli_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n",
"etat_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv')"
],
"execution_count": null,
"outputs": []
},
{
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}
},
"source": [
"print(deces_df.shape)\n",
"print(confirme_df.shape)\n",
"print(retabli_df.shape)\n",
"print(etat_df.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(264, 97)\n",
"(264, 97)\n",
"(250, 97)\n",
"(185, 14)\n"
],
"name": "stdout"
}
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},
{
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}
},
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],
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{
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"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Country_Region | \n",
" Last_Update | \n",
" Lat | \n",
" Long_ | \n",
" Confirmed | \n",
" Deaths | \n",
" Recovered | \n",
" Active | \n",
" Incident_Rate | \n",
" People_Tested | \n",
" People_Hospitalized | \n",
" Mortality_Rate | \n",
" UID | \n",
" ISO3 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Australia | \n",
" 2020-04-24 09:31:37 | \n",
" -25.0000 | \n",
" 133.0000 | \n",
" 6661 | \n",
" 75 | \n",
" 4124 | \n",
" 2462 | \n",
" 26.162916 | \n",
" NaN | \n",
" NaN | \n",
" 1.125957 | \n",
" 36 | \n",
" AUS | \n",
"
\n",
" \n",
" 1 | \n",
" Austria | \n",
" 2020-04-24 09:30:31 | \n",
" 47.5162 | \n",
" 14.5501 | \n",
" 15002 | \n",
" 522 | \n",
" 11872 | \n",
" 2608 | \n",
" 166.570439 | \n",
" NaN | \n",
" NaN | \n",
" 3.479536 | \n",
" 40 | \n",
" AUT | \n",
"
\n",
" \n",
" 2 | \n",
" Canada | \n",
" 2020-04-24 09:31:20 | \n",
" 60.0010 | \n",
" -95.0010 | \n",
" 43286 | \n",
" 2241 | \n",
" 14761 | \n",
" 26284 | \n",
" 114.344729 | \n",
" NaN | \n",
" NaN | \n",
" 5.177194 | \n",
" 124 | \n",
" CAN | \n",
"
\n",
" \n",
" 3 | \n",
" China | \n",
" 2020-04-24 06:54:42 | \n",
" 30.5928 | \n",
" 114.3055 | \n",
" 83884 | \n",
" 4636 | \n",
" 77997 | \n",
" 1251 | \n",
" 5.971767 | \n",
" NaN | \n",
" NaN | \n",
" 5.526680 | \n",
" 156 | \n",
" CHN | \n",
"
\n",
" \n",
" 4 | \n",
" Denmark | \n",
" 2020-04-24 09:30:31 | \n",
" 56.0000 | \n",
" 10.0000 | \n",
" 8408 | \n",
" 394 | \n",
" 5573 | \n",
" 2441 | \n",
" 145.160658 | \n",
" NaN | \n",
" NaN | \n",
" 4.686013 | \n",
" 208 | \n",
" DNK | \n",
"
\n",
" \n",
" 5 | \n",
" Finland | \n",
" 2020-04-24 09:30:31 | \n",
" 61.9241 | \n",
" 25.7482 | \n",
" 4284 | \n",
" 172 | \n",
" 2000 | \n",
" 2112 | \n",
" 77.318499 | \n",
" NaN | \n",
" NaN | \n",
" 4.014939 | \n",
" 246 | \n",
" FIN | \n",
"
\n",
" \n",
" 6 | \n",
" France | \n",
" 2020-04-24 09:30:31 | \n",
" 46.2276 | \n",
" 2.2137 | \n",
" 159467 | \n",
" 21889 | \n",
" 42773 | \n",
" 94805 | \n",
" 244.305837 | \n",
" NaN | \n",
" NaN | \n",
" 13.726351 | \n",
" 250 | \n",
" FRA | \n",
"
\n",
" \n",
" 7 | \n",
" Germany | \n",
" 2020-04-24 09:30:31 | \n",
" 51.1657 | \n",
" 10.4515 | \n",
" 153129 | \n",
" 5575 | \n",
" 106800 | \n",
" 40754 | \n",
" 182.766519 | \n",
" NaN | \n",
" NaN | \n",
" 3.640721 | \n",
" 276 | \n",
" DEU | \n",
"
\n",
" \n",
" 8 | \n",
" Iceland | \n",
" 2020-04-24 09:30:31 | \n",
" 64.9631 | \n",
" -19.0208 | \n",
" 1789 | \n",
" 10 | \n",
" 1509 | \n",
" 270 | \n",
" 524.249084 | \n",
" NaN | \n",
" NaN | \n",
" 0.558971 | \n",
" 352 | \n",
" ISL | \n",
"
\n",
" \n",
" 9 | \n",
" Ireland | \n",
" 2020-04-24 09:30:31 | \n",
" 53.1424 | \n",
" -7.6921 | \n",
" 17607 | \n",
" 794 | \n",
" 9233 | \n",
" 7580 | \n",
" 356.576092 | \n",
" NaN | \n",
" NaN | \n",
" 4.509570 | \n",
" 372 | \n",
" IRL | \n",
"
\n",
" \n",
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\n",
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"
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"text/plain": [
" Country_Region Last_Update Lat ... Mortality_Rate UID ISO3\n",
"0 Australia 2020-04-24 09:31:37 -25.0000 ... 1.125957 36 AUS\n",
"1 Austria 2020-04-24 09:30:31 47.5162 ... 3.479536 40 AUT\n",
"2 Canada 2020-04-24 09:31:20 60.0010 ... 5.177194 124 CAN\n",
"3 China 2020-04-24 06:54:42 30.5928 ... 5.526680 156 CHN\n",
"4 Denmark 2020-04-24 09:30:31 56.0000 ... 4.686013 208 DNK\n",
"5 Finland 2020-04-24 09:30:31 61.9241 ... 4.014939 246 FIN\n",
"6 France 2020-04-24 09:30:31 46.2276 ... 13.726351 250 FRA\n",
"7 Germany 2020-04-24 09:30:31 51.1657 ... 3.640721 276 DEU\n",
"8 Iceland 2020-04-24 09:30:31 64.9631 ... 0.558971 352 ISL\n",
"9 Ireland 2020-04-24 09:30:31 53.1424 ... 4.509570 372 IRL\n",
"\n",
"[10 rows x 14 columns]"
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"source": [
"country=['China','Germany','Morocco']\n",
"etat_df[etat_df['Country_Region'].isin(country)]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Country_Region | \n",
" Last_Update | \n",
" Lat | \n",
" Long_ | \n",
" Confirmed | \n",
" Deaths | \n",
" Recovered | \n",
" Active | \n",
" Incident_Rate | \n",
" People_Tested | \n",
" People_Hospitalized | \n",
" Mortality_Rate | \n",
" UID | \n",
" ISO3 | \n",
"
\n",
" \n",
" \n",
" \n",
" 3 | \n",
" China | \n",
" 2020-04-24 06:54:42 | \n",
" 30.5928 | \n",
" 114.3055 | \n",
" 83884 | \n",
" 4636 | \n",
" 77997 | \n",
" 1251 | \n",
" 5.971767 | \n",
" NaN | \n",
" NaN | \n",
" 5.526680 | \n",
" 156 | \n",
" CHN | \n",
"
\n",
" \n",
" 7 | \n",
" Germany | \n",
" 2020-04-24 09:30:31 | \n",
" 51.1657 | \n",
" 10.4515 | \n",
" 153129 | \n",
" 5575 | \n",
" 106800 | \n",
" 40754 | \n",
" 182.766519 | \n",
" NaN | \n",
" NaN | \n",
" 3.640721 | \n",
" 276 | \n",
" DEU | \n",
"
\n",
" \n",
" 123 | \n",
" Morocco | \n",
" 2020-04-24 09:30:31 | \n",
" 31.7917 | \n",
" -7.0926 | \n",
" 3568 | \n",
" 155 | \n",
" 456 | \n",
" 2957 | \n",
" 9.666611 | \n",
" NaN | \n",
" NaN | \n",
" 4.344170 | \n",
" 504 | \n",
" MAR | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Country_Region Last_Update Lat ... Mortality_Rate UID ISO3\n",
"3 China 2020-04-24 06:54:42 30.5928 ... 5.526680 156 CHN\n",
"7 Germany 2020-04-24 09:30:31 51.1657 ... 3.640721 276 DEU\n",
"123 Morocco 2020-04-24 09:30:31 31.7917 ... 4.344170 504 MAR\n",
"\n",
"[3 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BwXwXnZivQxE"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JP-pW9D-uVGh",
"outputId": "3c1d325e-1e19-4855-9383-005a79a44169",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
}
},
"source": [
"etat_df.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Country_Region', 'Last_Update', 'Lat', 'Long_', 'Confirmed', 'Deaths',\n",
" 'Recovered', 'Active', 'Incident_Rate', 'People_Tested',\n",
" 'People_Hospitalized', 'Mortality_Rate', 'UID', 'ISO3'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cbtH3o1vt14l",
"outputId": "77a7096f-ab73-42c2-bd58-319c639a328c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
}
},
"source": [
"# Afficher les statistiques au niveau monde\n",
"global_data=etat_df.copy().drop(['Country_Region','Last_Update', 'Lat', 'Long_','Active', 'Incident_Rate', 'People_Tested',\n",
" 'People_Hospitalized', 'Mortality_Rate', 'UID', 'ISO3'],axis=1)\n",
"global_synthese=pd.DataFrame(global_data.sum()).transpose()\n",
"global_synthese"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Confirmed | \n",
" Deaths | \n",
" Recovered | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1999628 | \n",
" 128011 | \n",
" 500996 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Confirmed Deaths Recovered\n",
"0 1999628 128011 500996"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QfMQjQI6vWiV",
"outputId": "00608bf1-6e16-4887-f668-d476fc7cb4f0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 460,
"referenced_widgets": [
"bfcc849d615049b2b07c216621e37d04",
"c6cfc4990301440abf3ad5da41265bf2",
"c987c7558e5c4a70837381de39a3dc99",
"a37a48900f7447f7ba0ae20ec4d8657f",
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"c8eaab8ee02749a29a2b26eb151e29d9",
"56fccb2f2f8a4303bc72af5100a295fa"
]
}
},
"source": [
"# Visualiser les pays les plus touchés\n",
"fig=go.FigureWidget(layout=go.Layout())\n",
"def highlight_col(x):\n",
" r='background-color:red'\n",
" y='background-color:purple'\n",
" g='background-color:grey'\n",
" df1=pd.DataFrame('',index=x.index,columns=x.columns)\n",
" df1.iloc[:,4]=y\n",
" df1.iloc[:,5]=r\n",
" df1.iloc[:,6]=g\n",
"\n",
" return df1\n",
"\n",
"def show_cases(n):\n",
" n=int(n)\n",
" return etat_df.sort_values('Confirmed',ascending=False).head(n).style.apply(highlight_col,axis=None)\n",
"\n",
"interact(show_cases,n='10')\n",
"\n",
"ipywLayout=widgets.Layout(border='solid 2px green')\n",
"ipywLayout.display='none'\n",
"widgets.VBox([fig],layout=ipywLayout)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bfcc849d615049b2b07c216621e37d04",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"interactive(children=(Text(value='10', description='n'), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c17cb374aadd4eae88159727337d94ea",
"version_minor": 0,
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},
"text/plain": [
"VBox(children=(FigureWidget({\n",
" 'data': [], 'layout': {'template': '...'}\n",
"}),), layout=Layout(border='solid …"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2wtmDopmEeTs",
"outputId": "31948ef0-3302-4b7a-980d-49f774b694c0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
}
},
"source": [
"etat_class_df= etat_df.sort_values('Confirmed',ascending=False)\n",
"etat_class_df.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Country_Region', 'Last_Update', 'Lat', 'Long_', 'Confirmed', 'Deaths',\n",
" 'Recovered', 'Active', 'Incident_Rate', 'People_Tested',\n",
" 'People_Hospitalized', 'Mortality_Rate', 'UID', 'ISO3'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JCZM_S3_FECb",
"outputId": "5f73599a-2f7b-4be1-87c3-a132cfae15c6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 518
}
},
"source": [
"etat_class_df.columns\n",
"px.bar(\n",
" etat_class_df.head(10),\n",
" x= 'Country_Region',\n",
" y= 'Confirmed',\n",
" title='Les 10 pays les plus touchés par coronavirus (cas confirmés)',\n",
" color_discrete_sequence=['pink'],\n",
" height=500,\n",
" width=800\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" \n",
" \n",
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\n",
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{
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"metadata": {
"id": "MPl1ZC9UF5tP",
"outputId": "119f93b4-592e-4b14-e857-6a06f46a47c3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 518
}
},
"source": [
"etat_class_df.columns\n",
"px.bar(\n",
" etat_class_df.head(10),\n",
" x= 'Country_Region',\n",
" y= 'Deaths',\n",
" title='Les 10 pays les plus touchés par coronavirus (cas decès)',\n",
" color_discrete_sequence=['pink'],\n",
" height=500,\n",
" width=800\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"
\n",
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\n",
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""
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{
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"metadata": {
"id": "WQ6BUQ6cGJ9t",
"outputId": "76946fcc-8494-49a0-dee7-c1dc842b1f40",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 518
}
},
"source": [
"etat_class_df.columns\n",
"px.bar(\n",
" etat_class_df.head(10),\n",
" x= 'Country_Region',\n",
" y= 'Recovered',\n",
" title='Les 10 pays les plus touchés par coronavirus (cas rétablis)',\n",
" color_discrete_sequence=['pink'],\n",
" height=500,\n",
" width=800\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"
\n",
" \n",
"
\n",
"\n",
""
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},
"metadata": {
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{
"cell_type": "code",
"metadata": {
"id": "A5vqvyoOGWQ4",
"outputId": "a21be283-fb21-4efb-af81-9fb71badd028",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
}
},
"source": [
"# Visualiser les données sur une carte\n",
"m=folium.Map(location=[31.7917,-7.0926],zoom_start=6, max_zoom=12,min_zoom=2)\n",
"for i in range(0,len(confirme_df)):\n",
" folium.Circle(\n",
" location=[confirme_df.iloc[i]['Lat'],confirme_df.iloc[i]['Long']],\n",
" fill=True,\n",
" radius=(int((np.log(confirme_df.iloc[i,-1]+1.00001)))+0.2)*50000,\n",
" color='red',\n",
" fill_color='indigo',\n",
" tooltip = \"\"+\n",
" \"
\"+confirme_df.iloc[i]['Country/Region'] + \"
\"\n",
" \"
\"+\n",
" \"
\"+\n",
" \"- Confirme: \"+str(confirme_df.iloc[i,-1])+\"
\"+\n",
" \"- Deces: \"+str(deces_df.iloc[i,-1])+\"
\"+\n",
" \"- Taux de mortalite: \"+ str(np.round(deces_df.iloc[i,-1]/(confirme_df.iloc[i,-1]+1.00001)*100,2))+ \"
\"+\n",
" \"
\",\n",
" ).add_to(m)\n",
"m\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dIozTDhcJoxu"
},
"source": [
"# Visualiser la situation dans un pays (maroc)\n",
"conf_mar=confirme_df[confirme_df['Country/Region']=='Morocco']\n",
"deces_mar=deces_df[deces_df['Country/Region']=='Morocco']\n",
"retabli_mar=retabli_df[retabli_df['Country/Region']=='Morocco']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Dxsj2DJnLoxa"
},
"source": [
"# Préparation des donées (MAroc)\n",
"conf_mar=conf_mar.copy().drop(['Province/State', 'Country/Region', 'Lat', 'Long'],axis=1).sum()\n",
"deces_mar=deces_mar.copy().drop(['Province/State', 'Country/Region', 'Lat', 'Long'],axis=1).sum()\n",
"retabli_mar=retabli_mar.copy().drop(['Province/State', 'Country/Region', 'Lat', 'Long'],axis=1).sum()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oNjtq9VmMkxy",
"outputId": "36756cc7-2c39-4a25-dea1-6cb3c344d191",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 448
}
},
"source": [
"# Dessin de la courbe\n",
"fig,ax=plt.subplots(figsize=(18,8))\n",
"ax.plot(conf_mar.index,conf_mar.values,label='Confirmé')\n",
"ax.plot(deces_mar.index,deces_mar.values,label='Decès')\n",
"ax.plot(retabli_mar.index,retabli_mar.values,label='Retabli')\n",
"\n",
"plt.xticks(rotation='vertical')\n",
"ax.set(xlabel='Date',ylabel='Nombre de Cas',title='Situation au Maroc')\n",
"plt.legend()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 74
},
{
"output_type": "display_data",
"data": {
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\n",
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