Update Readme.md

journal/Readme.md

@@ -201,3 +201,70 @@ data = data_raw
 MARKDOWN : "Une fois nos données disponibles, je vais chercher les lignes des pays nous intéressant avec la fonction `.loc()` de la bibliothèque Pandas. Une fois ces données localisées, je vais les regrouper sous forme de DataFrame afin de pouvoir les traiter."
 
 Pour avoir le `.loc()` écrire ``<code>.loc()</code>``
+
+Nous pouvons maintenant extraire les données pour chaque pays. Pour cela, il faut tout d'abord les localiser avec la fonction `.loc()` en écrivant le code suivant :
+
+```
+Row_Belgium = data.loc[data['Country/Region'] == 'Belgium']
+Series_Belgium = pd.Series(data.loc[Row_Belgium.index[0]], name = 'Belgium', dtype = int)
+
+Row_China = data.loc[(data['Country/Region'] == 'China') & (data['Province/State'] != 'Hong Kong')]
+Series_China_Total = Row_China.sum()
+
+Row_HongKong = data.loc[data['Province/State'] == 'Hong Kong']
+Series_HongKong = pd.Series(data.loc[Row_HongKong.index[0]], name = 'HongKong', dtype = int)
+
+Row_France = data.loc[(data['Province/State'].isna()) & (data['Country/Region'] == 'France')]
+Series_France = pd.Series(data.loc[Row_France.index[0]], name = 'France', dtype = int)
+
+Row_Germany = data.loc[data['Country/Region'] == 'Germany']
+Series_Germany = pd.Series(data.loc[Row_Germany.index[0]], name = 'Germany', dtype = int)
+
+Row_Iran = data.loc[data['Country/Region'] == 'Iran']
+Series_Iran = pd.Series(data.loc[Row_Iran.index[0]], name = 'Iran', dtype = int)
+
+Row_Italy = data.loc[data['Country/Region'] == 'Italy']
+Series_Italy = pd.Series(data.loc[Row_Italy.index[0]], name = 'Italy', dtype = int)
+
+Row_Japan = data.loc[data['Country/Region'] == 'Japan']
+Series_Japan = pd.Series(data.loc[Row_Japan.index[0]], name = 'Japan', dtype = int)
+
+Row_KoreaSouth = data.loc[data['Country/Region'] == 'Korea, South']
+Series_KoreaSouth = pd.Series(data.loc[Row_KoreaSouth.index[0]], name = 'Korea, South', dtype = int)
+
+Row_Netherlands = data.loc[(data['Province/State'].isna()) & (data['Country/Region'] == 'Netherlands')]
+Series_Netherlands = pd.Series(data.loc[Row_Netherlands.index[0]], name = 'Netherlands', dtype = int)
+
+Row_Portugal = data.loc[data['Country/Region'] == 'Portugal']
+Series_Portugal = pd.Series(data.loc[Row_Portugal.index[0]], name = 'Portugal', dtype = int)
+
+Row_Spain = data.loc[data['Country/Region'] == 'Spain']
+Series_Spain = pd.Series(data.loc[Row_Spain.index[0]], name = 'Spain', dtype = int)
+
+Row_UnitedKingdom = data.loc[(data['Province/State'].isna()) & (data['Country/Region'] == 'United Kingdom')]
+Series_UnitedKingdom = pd.Series(data.loc[Row_UnitedKingdom.index[0]], name = 'United Kingdom', dtype = int)
+
+Row_US = data.loc[data['Country/Region'] == 'US']
+Series_US = pd.Series(data.loc[Row_US.index[0]], name = 'US', dtype = int)
+
+frame = {'Belgium': Series_Belgium, 
+         'China': Series_China_Total, 
+         'Hong Kong': Series_HongKong, 
+         'France': Series_France, 
+         'Germany': Series_Germany, 
+         'Iran': Series_Iran, 
+         'Italy': Series_Italy, 
+         'Japan': Series_Japan, 
+         'Korea, South': Series_KoreaSouth, 
+         'Netherlands': Series_Netherlands, 
+         'Portugal': Series_Portugal, 
+         'Spain': Series_Spain, 
+         'United Kingdom': Series_UnitedKingdom, 
+         'US': Series_US}
+
+selected_data_raw = pd.DataFrame(frame)
+selected_data = selected_data_raw[4:].reset_index()
+selected_data = selected_data.rename(columns={'index': 'Date'})
+# display(selected_data)
+```
+