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#!/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[ ]:
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