File size: 18,479 Bytes
0edf40d 8470e60 0edf40d 8470e60 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 1c78e6d 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d 16ea8f5 0edf40d f54d021 16ea8f5 f54d021 16ea8f5 f54d021 0edf40d 16ea8f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 |
#!/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")
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.
#
# 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]:
# In[18]:
import geopandas as gpd
import folium
# 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[75]:
# # 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]:
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)
|