Upload Choropleth_Sanitary.py

Choropleth_Sanitary.py

@@ -0,0 +1,537 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# Notes:
+# 
+# Les scores de responsabilité varient de -4 à 4 et définissent la propreté du point de vu des agents sanitaires ou des foyers. -4 implique que la slubrité est due aux foyers tandis que 4 implique la salubrité est due aux agents.
+# 
+# Le score propreté quant à lui décris le niveau de propreté global en faisant une moyenne des scores des deux parties.
+
+# In[1]:
+
+
+get_ipython().system(' pip install geopandas plotly pandas folium')
+
+
+# ## Generating dummy data
+
+# In[2]:
+
+
+import numpy as np
+import pandas as pd
+import random
+import json
+import plotly.express as px
+
+
+# Données de test: Il y a 4 foyers par quartier et 10 quartiers répartis dans 2 communes pour faire les test.  
+# 
+# NB: En nomenclature, communauté est confondue avec région et quartier avec préfecture.
+
+# In[3]:
+
+
+DATA = [
+    {'foyer': 1, 'quartier_id':1, "community_id": 0, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 2, 'quartier_id':1, "community_id": 0, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 3, 'quartier_id':1, "community_id": 0, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':1, "community_id": 0, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 5, 'quartier_id':2, "community_id": 0, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 4},
+    {'foyer': 1, 'quartier_id':2, "community_id": 0, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':2, "community_id": 0, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':2, "community_id": 0, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':2, "community_id": 0, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':3, "community_id": 0, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':3, "community_id": 0, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 4, 'score': 4},
+    {'foyer': 3, 'quartier_id':3, "community_id": 0, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 2, 'score': 3},
+    {'foyer': 4, 'quartier_id':3, "community_id": 0, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':4, "community_id": 0, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':4, "community_id": 0, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':4, "community_id": 0, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':4, "community_id": 0, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+
+    {'foyer': 1, 'quartier_id':5, "community_id": 4, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 2, 'quartier_id':5, "community_id": 4, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 3, 'quartier_id':5, "community_id": 4, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':5, "community_id": 4, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 5, 'quartier_id':5, "community_id": 4, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 4},
+    {'foyer': 1, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 4, 'score': 4},
+    {'foyer': 3, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 2, 'score': 3},
+    {'foyer': 4, 'quartier_id':6, "community_id": 4, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':7, "community_id": 4, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':7, "community_id": 4, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':7, "community_id": 4, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':7, "community_id": 4, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+
+    {'foyer': 1, 'quartier_id':8, "community_id": 9, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 2, 'score_foyer': 3, 'score': 5/2},
+    {'foyer': 2, 'quartier_id':8, "community_id": 9, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 0, 'score': 2},
+    {'foyer': 3, 'quartier_id':8, "community_id": 9, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':8, "community_id": 9, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 5, 'quartier_id':9, "community_id": 9, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 4},
+    {'foyer': 1, 'quartier_id':9, "community_id": 9, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':9, "community_id": 9, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':9, "community_id": 9, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 0, 'score_foyer': 0, 'score': 0},
+    {'foyer': 4, 'quartier_id':9, "community_id": 9, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':10, "community_id": 9, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':10, "community_id": 9, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 4, 'score': 4},
+    {'foyer': 3, 'quartier_id':10, "community_id": 9, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 2, 'score': 3},
+    {'foyer': 4, 'quartier_id':10, "community_id": 9, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':10, "community_id": 9, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 0, 'score_foyer': 0, 'score': 0},
+    {'foyer': 2, 'quartier_id':11, "community_id": 9, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':11, "community_id": 9, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 1, 'score_foyer': 1, 'score': 1},
+    {'foyer': 4, 'quartier_id':11, "community_id": 9, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+
+    {'foyer': 1, 'quartier_id':16, "community_id": 5, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 2, 'quartier_id':16, "community_id": 5, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 3, 'quartier_id':16, "community_id": 5, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':16, "community_id": 5, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 5, 'quartier_id':21, "community_id": 5, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 4},
+    {'foyer': 1, 'quartier_id':21, "community_id": 5, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':21, "community_id": 5, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':21, "community_id": 5, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':21, "community_id": 5, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':31, "community_id": 5, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':31, "community_id": 5, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 4, 'score': 4},
+    {'foyer': 3, 'quartier_id':31, "community_id": 5, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 2, 'score': 3},
+    {'foyer': 4, 'quartier_id':31, "community_id": 5, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':24, "community_id": 5, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':24, "community_id": 5, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':24, "community_id": 5, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':24, "community_id": 5, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+
+    {'foyer': 1, 'quartier_id':17, "community_id": 6, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 1, 'score_foyer': 3, 'score': 1},
+    {'foyer': 2, 'quartier_id':17, "community_id": 6, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 3, 'quartier_id':17, "community_id": 6, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':17, "community_id": 6, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 5, 'quartier_id':25, "community_id": 6, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 4},
+    {'foyer': 1, 'quartier_id':25, "community_id": 6, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 1, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':25, "community_id": 6, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':25, "community_id": 6, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':25, "community_id": 6, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':32, "community_id": 6, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':32, "community_id": 6, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 4, 'score': 4},
+    {'foyer': 3, 'quartier_id':32, "community_id": 6, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 2, 'score': 3},
+    {'foyer': 4, 'quartier_id':32, "community_id": 6, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 3, 'score_foyer': 3, 'score': 3},
+    {'foyer': 1, 'quartier_id':23, "community_id": 6, 'nom': 'Foyer de Di', "mois": 1, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 2, 'quartier_id':23, "community_id": 6, 'nom': 'Foyer de Di', "mois": 2, 'annee':2000, 'score_sanitaire': 5, 'score_foyer': 5, 'score': 5},
+    {'foyer': 3, 'quartier_id':24, "community_id": 6, 'nom': 'Foyer de Di', "mois": 3, 'annee':2000, 'score_sanitaire': 4, 'score_foyer': 3, 'score': 1},
+    {'foyer': 4, 'quartier_id':25, "community_id": 6, 'nom': 'Foyer de Di', "mois": 4, 'annee':2000, 'score_sanitaire': 2, 'score_foyer': 3, 'score': 3},
+]
+
+
+# In[4]:
+
+
+data = pd.DataFrame(DATA)
+data.head()
+
+
+# In[5]:
+
+
+data['score'] = (data['score_sanitaire'] + data['score_foyer']) / 2
+data.head(2)
+
+
+# In[6]:
+
+
+data['score responsabilité'] = data['score_sanitaire'] - data['score_foyer']
+
+
+# In[7]:
+
+
+data.head()
+
+
+# In[8]:
+
+
+np.average(data['score'], axis=0, weights=data.index)
+
+
+# In[9]:
+
+
+def moyenne_par_quartier(quartiers, id, scoring="score"):
+  quartier = quartiers[quartiers.quartier_id == id]
+  return quartier[scoring].mean()
+
+
+# In[10]:
+
+
+moyenne_par_quartier(data, 2)
+
+
+# Moyenne pondérée pour les quartiers qui ont peu de foyers enrégistrés dans une communauté.
+
+# In[11]:
+
+
+def moyenne_par_communaute(data, community_id, scoring="score"):
+  community = data[data.community_id == community_id]
+  avg = np.average(community[scoring], axis=0, weights=community.index)
+  return avg
+
+
+# In[12]:
+
+
+moyenne_par_communaute(data, 4)
+
+
+# In[13]:
+
+
+def moyenne_par_mois_par_communaute(data, community_id, month, scoring="score"):
+  filtered = data[(data.community_id == community_id) & (data.mois == month)]
+  avg = np.average(filtered[scoring], axis=0, weights=filtered.index)
+  return avg
+
+
+# In[14]:
+
+
+def moyenne_par_mois_par_quartier(data, quartier_id, month, scoring="score"):
+  filtered = data[(data.quartier_id == quartier_id) & (data.mois == month)]
+  avg = np.average(filtered[scoring], axis=0, weights=filtered.index)
+  return avg
+
+
+# In[15]:
+
+
+def moyenne_par_annee_par_communaute(data, community_id, year, scoring="score"):
+  filtered = data[(data.community_id == community_id) & (data.annee == year)]
+  avg = np.average(filtered[scoring], axis=0, weights=filtered.index)
+  return avg
+
+
+# In[16]:
+
+
+def moyenne_par_annee_par_quartier(data, quartier_id, year, scoring="score"):
+  filtered = data[(data.quartier_id == quartier_id) & (data.mois == year)]
+  avg = np.average(filtered[scoring], axis=0, weights=filtered.index)
+  return avg
+
+
+# ##Plot Map
+
+# In[17]:
+
+
+import plotly.io as pio
+pio.renderers.default = 'browser'
+
+
+# In[18]:
+
+
+import geopandas as gpd
+import folium
+from IPython.display import display
+
+# Specify the path to your GeoJSON file
+geojson_file_path = 'BNDA_TGO_2017-06-29_lastupdate.geojson'
+geojson_data = json.load(open(geojson_file_path, "r"))
+# Read the GeoJSON file using geopandas
+gdf = gpd.read_file(geojson_file_path)
+
+
+# On définit ici quelques outils pour faire la correspondance id vers quartier et communauté (vice-versa).
+
+# In[19]:
+
+
+id_quartier = {}
+
+for row in gdf.iterrows():
+  id_quartier[row[0]] = row[1][4]
+#id_quartier
+
+
+# In[20]:
+
+
+quartier_id = {}
+
+for row in gdf.iterrows():
+  quartier_id[row[1][4]] = row[0]
+
+
+# In[21]:
+
+
+id_regions = {}
+
+for row in gdf.iterrows():
+  if row[1][3] not in id_regions.values():
+    id_regions[row[0]] = row[1][3]
+
+
+# In[22]:
+
+
+id_regions
+
+
+# In[23]:
+
+
+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()
+
+
+# In[ ]:
+
+
+
+
+
+# In[25]:
+
+
+quartiers = data['quartier_name'].unique().tolist()
+
+
+# In[26]:
+
+
+qm = {}
+for q in quartiers:
+  qm[q] = moyenne_par_quartier(data, quartier_id[q])
+
+#qm
+
+
+# In[27]:
+
+
+ids = [quartier_id[q] for q in quartiers]
+
+
+# In[28]:
+
+
+quartiers = list(qm.keys())
+
+
+# In[29]:
+
+
+scores = list(qm.values())
+
+
+# #Scores de propreté - Par quartiers (préfectures)
+
+# In[30]:
+
+
+new_df = pd.DataFrame(data={
+    'quartier': quartiers,
+    'scores': scores,
+    "quartier_id": ids
+})
+new_df.head()
+
+
+# In[31]:
+
+
+new_df.to_csv('new_df.csv', index=False)
+
+
+# In[39]:
+
+
+qs = new_df['quartier'].tolist()
+new_gdf_q = gdf[gdf.adm2nm.isin(qs)]
+
+
+# In[59]:
+
+
+gdf_merged_q = pd.merge(new_gdf_q, new_df, how='left', left_on="adm2nm", right_on="quartier")
+
+
+# In[60]:
+
+
+gdf_merged_q.head()
+
+
+# In[61]:
+
+
+geojson = gdf_merged_q.__geo_interface__
+
+
+# In[109]:
+
+
+geojson
+
+
+# In[75]:
+
+
+gdf_merged_q[gdf_merged_q['adm2nm'] == "Blitta"]
+
+
+# # Map Scores de propreté pour les Préfectures du Togo
+
+# Note: la carte est centrée. Il faut zoomer en arrière pour avoir le rendu.
+
+# 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})
+fig.show()
+
+
+# In[101]:
+
+
+gdf_merged_q.to_csv('merged_q.csv', index=False)
+
+
+# In[ ]:
+
+
+
+
+
+# # Scores de propreté - par régions
+
+# In[78]:
+
+
+id_regions
+
+
+# In[79]:
+
+
+regions_id = list(id_regions.keys())
+scores = list()
+
+
+# In[80]:
+
+
+rm = {}
+for q in regions_id:
+  print(q)
+  rm[q] = moyenne_par_communaute(data, q)
+
+rm
+
+
+# In[81]:
+
+
+regions = [id_regions[i] for i in regions_id]
+scores = list(rm.values())
+
+
+# In[82]:
+
+
+region_df = pd.DataFrame({
+    'region_id': regions_id,
+    'region': regions,
+    'scores': scores
+})
+
+region_df.head()
+
+
+# In[ ]:
+
+
+
+
+
+# # Score de responsabilité - par quartiers (Préfectures)
+
+# In[93]:
+
+
+qm = {}
+for q in quartiers:
+    qm[q] = moyenne_par_quartier(data, quartier_id[q], scoring="score responsabilité")
+
+
+# In[94]:
+
+
+ids = [quartier_id[q] for q in quartiers]
+quartiers = list(qm.keys())
+scores = list(qm.values())
+
+
+# In[95]:
+
+
+respon_df =  pd.DataFrame(data={
+    'quartier': quartiers,
+    '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[ ]:
+
+
+
+
+
+# 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})
+fig.show()
+
+
+# In[ ]:
+
+
+
+