2 Commits
567a532c37
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c5d91b9c73
| Author | SHA1 | Message | Date |
|---|---|---|---|
 l_facheux
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c5d91b9c73 |
Merge pull request 'version beta' (#1) from beta into master
Reviewed-on: #1 |
2 years ago |
|
l_facheux
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6f2da2f5f7 |
version beta
|
2 years ago |
6 changed files with 1564 additions and 0 deletions
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70Colonnes.py
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16Doublons.py
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1386Table_final.csv
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19Values.py
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69main.py
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4requirements.txt
@ -0,0 +1,70 @@ |
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#Importation des données dont nous aurons besoin |
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from typing import List |
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import pandas as pd |
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import numpy as np |
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import requests |
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import csv |
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import re |
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|
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#Afficher les tableaux de données |
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datafram : pd.read_csv("C:\\Users\\luigg\\Data_cleaning\\Table_final.csv") |
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datafram.head(5) |
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|
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#Supprimer les colonnes inutilisées ou non pertinentes |
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to_drop : [''identifiant |
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','adresse |
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','commune |
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','coordonnees_x |
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','coordonnees_y |
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','code_epsg |
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','code_ape |
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','libelle_ape |
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','code_eprtr |
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','libelle_eprtr |
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','sigleUniteLegale_imp |
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','activitePrincipaleUniteLegale_imp |
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','Catégorie_entreprise_imp |
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','numeroVoieEtablissement_imp |
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','typeVoieEtablissement_imp |
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','libelleVoieEtablissement_imp |
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','libelleCommuneEtablissement_imp |
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','codeCommuneEtablissement_imp |
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','adresse_imp |
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','geo_imp |
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','com_code_imp |
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','code_commune_imp |
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','Code Officiel_EPCI_imp |
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','Code_Officiel_region_imp |
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','codenaffix_imp |
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','Intitule_NAF_imp |
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','groupe_imp |
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','division_imp |
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','nom_etablissement_tndan |
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','code_operation_eliminatio_valorisation_tndan |
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','libelle_operation_eliminatio_valorisation_tndan |
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','code_departement_tndan |
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','pays_tndan |
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','pays_pdan |
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','code_dechet_pdan |
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','libelle_dechet_pdan |
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','quantite_pdan |
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','unite_pdan |
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','code_operation_eliminatio_valorisation_pndan |
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','libelle_operation_eliminatio_valorisation_pndan |
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','code_departement_pndan |
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','pays_pndan |
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','code_dechet_pndan |
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','libelle_dechet_pndan |
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','quantite_pndan |
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','unite_pndan |
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','code_operation_eliminatio_valorisation_tdan |
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','libelle_operation_eliminatio_valorisation_tdan |
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','code_departement_tdan |
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','pays_tdan |
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','code_dechet_tdan |
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','libelle_dechet_tdan |
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'] |
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|
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datafram.drop(to_drop, inplace = True, axis = 1) |
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datafram.head(5) |
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|
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@ -0,0 +1,16 @@ |
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import panda as pd |
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import numpy as np |
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import csv |
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import re |
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|
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#Afficher les tableaux de données |
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datafram = pd.read_csv(r"C:\Users\luigg\Data_cleaning\Table_final.csv") |
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datafram.head(5) |
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|
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#Supprimer les doublons dans excel |
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nouvelle_table = datafram.drop_duplicates( |
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subset = ['order_id', 'customer_id'], |
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keep = 'last').reset_index(drop = True) |
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|
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#Afficher la nouvelle table |
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print(nouvelle_table) |
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1386
Table_final.csv
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@ -0,0 +1,19 @@ |
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import pandas as pd |
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import numpy as np |
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import csv |
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|
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#Afficher les tableaux de données |
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table = pd.read_csv("C:\Users\luigg\Data_cleaning\Table_final.csv") |
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table.head(5) |
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|
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#Remplacer les valeurs des lignes |
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|
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Replace_values = {0: 'Non', 1: 'Oui'} |
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|
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table = table.replace({"engagement_manifeste_imp |
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","engagement_data_imp |
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","prelevements_eaux_souterraines_pre |
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","prelevements_mer_pre |
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": replace_values}) |
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|
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table.head(5) |
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@ -0,0 +1,69 @@ |
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#Importation des données dont nous aurons besoin |
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import pandas as pd |
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import numpy as np |
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import csv |
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import re |
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|
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#Afficher les tableaux de données |
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df = pd.read_csv("C:\Users\luigg\Data_cleaning\Table_final.csv") |
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df.head(5) |
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|
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#Supprimer les colonnes inutilisées ou non pertinentes |
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|
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to_drop = ['identifiant |
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','adresse |
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','commune |
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','coordonnees_x |
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','coordonnees_y |
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','code_epsg |
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','code_ape |
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','libelle_ape |
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','code_eprtr |
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','libelle_eprtr |
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','sigleUniteLegale_imp |
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','activitePrincipaleUniteLegale_imp |
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','Catégorie_entreprise_imp |
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','numeroVoieEtablissement_imp |
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','typeVoieEtablissement_imp |
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','libelleVoieEtablissement_imp |
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','libelleCommuneEtablissement_imp |
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','codeCommuneEtablissement_imp |
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','adresse_imp |
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','geo_imp |
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','com_code_imp |
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','code_commune_imp |
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','Code Officiel_EPCI_imp |
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','Code_Officiel_region_imp |
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','codenaffix_imp |
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','Intitule_NAF_imp |
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','groupe_imp |
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','division_imp |
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','nom_etablissement_tndan |
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','code_operation_eliminatio_valorisation_tndan |
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','libelle_operation_eliminatio_valorisation_tndan |
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','code_departement_tndan |
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','pays_tndan |
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','pays_pdan |
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','code_dechet_pdan |
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','libelle_dechet_pdan |
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','quantite_pdan |
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','unite_pdan |
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','code_operation_eliminatio_valorisation_pndan |
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','libelle_operation_eliminatio_valorisation_pndan |
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','code_departement_pndan |
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','pays_pndan |
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','code_dechet_pndan |
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','libelle_dechet_pndan |
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','quantite_pndan |
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','unite_pndan |
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','code_operation_eliminatio_valorisation_tdan |
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','libelle_operation_eliminatio_valorisation_tdan |
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','code_departement_tdan |
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','pays_tdan |
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','code_dechet_tdan |
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','libelle_dechet_tdan |
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'] |
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|
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df.drop(to_drop, inplace = True, axis = 1) |
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df.head(5) |
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|
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@ -0,0 +1,4 @@ |
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pandas |
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numpy |
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csv |
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re |
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