Update app.py

app.py

@@ -25,6 +25,7 @@ import plotly.express as px
 import streamlit as st
 
 st.set_page_config(layout="wide")
+col1, col2 = st.columns(2)
 
 # Données de test: Il y a 4 foyers par quartier et 10 quartiers répartis dans 2 communes pour faire les test.  
 # 
@@ -130,32 +131,32 @@ DATA = [
 
 
 data = pd.DataFrame(DATA)
-data.head()
+#data.head()
 
 
 # In[5]:
 
 
 data['score'] = (data['score_sanitaire'] + data['score_foyer']) / 2
-data.head(2)
+#data.head(2)
 
 
 # In[6]:
 
 
-data['score responsabilité'] = data['score_sanitaire'] - data['score_foyer']
+#data['score responsabilité'] = data['score_sanitaire'] - data['score_foyer']
 
 
 # In[7]:
 
 
-data.head()
+#data.head()
 
 
 # In[8]:
 
 
-np.average(data['score'], axis=0, weights=data.index)
+#np.average(data['score'], axis=0, weights=data.index)
 
 
 # In[9]:
@@ -169,7 +170,7 @@ def moyenne_par_quartier(quartiers, id, scoring="score"):
 # In[10]:
 
 
-moyenne_par_quartier(data, 2)
+#moyenne_par_quartier(data, 2)
 
 
 # Moyenne pondérée pour les quartiers qui ont peu de foyers enrégistrés dans une communauté.
@@ -186,7 +187,7 @@ def moyenne_par_communaute(data, community_id, scoring="score"):
 # In[12]:
 
 
-moyenne_par_communaute(data, 4)
+#moyenne_par_communaute(data, 4)
 
 
 # In[13]:
@@ -281,7 +282,7 @@ for row in gdf.iterrows():
 # In[22]:
 
 
-id_regions
+#id_regions
 
 
 # In[23]:
@@ -289,7 +290,7 @@ id_regions
 
 data['quartier_name'] = data['quartier_id'].apply(lambda x: id_quartier[x])
 data['community_name'] = data['community_id'].apply(lambda x: id_regions[x])
-data.head()
+#data.head()
 
 
 # In[ ]:
@@ -342,13 +343,13 @@ new_df = pd.DataFrame(data={
     'scores': scores,
     "quartier_id": ids
 })
-new_df.head()
+#new_df.head()
 
 
 # In[31]:
 
 
-new_df.to_csv('new_df.csv', index=False)
+#new_df.to_csv('new_df.csv', index=False)
 
 
 # In[39]:
@@ -367,7 +368,7 @@ gdf_merged_q = pd.merge(new_gdf_q, new_df, how='left', left_on="adm2nm", right_o
 # In[60]:
 
 
-gdf_merged_q.head()
+#gdf_merged_q.head()
 
 
 # In[61]:
@@ -394,22 +395,24 @@ gdf_merged_q[gdf_merged_q['adm2nm'] == "Blitta"]
 
 # In[86]:
 
-
-fig = px.choropleth_mapbox(gdf_merged_q, 
-                          geojson=geojson,
-                           locations=gdf_merged_q.index,
-                           color='scores',
-                           mapbox_style="carto-positron",
-                           title="Scores de Propreté Pour Les Préfectures Du Togo",
-                           hover_name="adm2nm",
-                           color_continuous_scale="Viridis"
-                          )
-fig.update_layout(margin={'r':0, 't':0, "l": 0, 'r': 0})
-st.plotly_chart(fig)
+with col1:
+    st.header('Score Propre')
+
+    fig = px.choropleth_mapbox(gdf_merged_q, 
+                              geojson=geojson,
+                               locations=gdf_merged_q.index,
+                               color='scores',
+                               mapbox_style="carto-positron",
+                               title="Scores de Propreté Pour Les Préfectures Du Togo",
+                               hover_name="adm2nm",
+                               color_continuous_scale="Viridis"
+                              )
+    fig.update_layout(margin={'r':0, 't':0, "l": 0, 'r': 0})
+    st.plotly_chart(fig)
 # In[101]:
 
 
-gdf_merged_q.to_csv('merged_q.csv', index=False)
+#gdf_merged_q.to_csv('merged_q.csv', index=False)
 
 
 # In[ ]:
@@ -423,7 +426,7 @@ gdf_merged_q.to_csv('merged_q.csv', index=False)
 # In[78]:
 
 
-id_regions
+#id_regions
 
 
 # In[79]:
@@ -441,7 +444,7 @@ for q in regions_id:
   print(q)
   rm[q] = moyenne_par_communaute(data, q)
 
-rm
+#rm
 
 
 # In[81]:
@@ -460,7 +463,6 @@ region_df = pd.DataFrame({
     'scores': scores
 })
 
-region_df.head()
 
 
 # In[ ]:
@@ -495,15 +497,12 @@ respon_df =  pd.DataFrame(data={
     'scores': scores,
     "quartier_id": ids
 })
-respon_df.head()
 
 
 # In[102]:
 
 
 gdf_merged_q_r = pd.merge(new_gdf_q, respon_df, left_on="adm2nm", right_on="quartier", how='left')
-gdf_merged_q_r.to_csv('merged_q_r.csv', index=False)
-gdf_merged_q_r.head()
 
 
 # In[ ]:
@@ -514,18 +513,20 @@ gdf_merged_q_r.head()
 
 # In[100]:
 
-
-fig = px.choropleth_mapbox(gdf_merged_q_r, 
-                          geojson=geojson,
-                           locations=gdf_merged_q.index,
-                           color='scores',
-                           mapbox_style="carto-positron",
-                           title="Scores de Propreté Pour Les Préfectures Du Togo",
-                           hover_name="adm2nm",
-                           color_continuous_scale="Viridis"
-                          )
-fig.update_layout(margin={'r':0, 't':0, "l": 0, 'r': 0})
-st.plotly_chart(fig)
+with col2:
+
+    st.header('Scores Responsable')
+    fig = px.choropleth_mapbox(gdf_merged_q_r, 
+                              geojson=geojson,
+                               locations=gdf_merged_q.index,
+                               color='scores',
+                               mapbox_style="carto-positron",
+                               title="Scores de Propreté Pour Les Préfectures Du Togo",
+                               hover_name="adm2nm",
+                               color_continuous_scale="Viridis"
+                              )
+    fig.update_layout(margin={'r':0, 't':0, "l": 0, 'r': 0})
+    st.plotly_chart(fig)
 
 
 # In[ ]: