From c67337b10e20cb72fd65c13ac9c28a9d0d608851 Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Wed, 14 Feb 2024 11:44:18 +0000 Subject: [PATCH] Correction export --- 0_Cleaning_and_merge.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/0_Cleaning_and_merge.py b/0_Cleaning_and_merge.py index a300041..9c9aac0 100644 --- a/0_Cleaning_and_merge.py +++ b/0_Cleaning_and_merge.py @@ -82,7 +82,7 @@ with fs.open(FILE_PATH_OUT_S3, 'w') as file_out: ## 2 - Construction of KPIs on a given period -def explanatory_variables(min_date = "2021-09-01", max_date = "2023-09-01", df_campaigns_information = df1_campaigns_information, df_products_purchased_reduced = df1_products_purchased_reduced, df_customerplus_clean = df1_customerplus_clean): +def explanatory_variables(min_date, max_date, df_campaigns_information = df1_campaigns_information, df_products_purchased_reduced = df1_products_purchased_reduced, df_customerplus_clean = df1_customerplus_clean): # Filtre de cohérence pour la mise en pratique de notre méthode max_date = pd.to_datetime(max_date, utc = True, format = 'ISO8601') @@ -138,7 +138,7 @@ def explanatory_variables(min_date = "2021-09-01", max_date = "2023-09-01", df_c return df_customer_product # Fonction pour créer les variables expliquée -def explained_variable(min_date = "2023-08-01", max_date = "2023-11-01", df_products_purchased_reduced = df1_products_purchased_reduced): +def explained_variable(min_date, max_date, df_products_purchased_reduced = df1_products_purchased_reduced): # Filtrer la base d'achat df_products_purchased_reduced = df_products_purchased_reduced[(df_products_purchased_reduced['purchase_date'] <= max_date) & (df_products_purchased_reduced['purchase_date'] > min_date)] @@ -185,7 +185,7 @@ FILE_KEY_OUT_S3 = "dataset_train.csv" FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3 with fs.open(FILE_PATH_OUT_S3, 'w') as file_out: - dataset_test.to_csv(file_out, index = False) + dataset_train.to_csv(file_out, index = False) print("Exportation dataset train : SUCCESS")