Choropleth_Sanitary.py

#!/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]:




# ## Generating dummy data

# In[2]:


import numpy as np
import pandas as pd
import random
import json
import plotly.express as px
import streamlit as st

st.set_page_config(layout="wide")

# 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})
st.plotly_chart(fig)
# 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})
st.plotly_chart(fig)


# In[ ]: