57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
# Business Data Challenge - Team 1
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import pandas as pd
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import numpy as np
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import os
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import s3fs
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import re
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import warnings
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# Create filesystem object
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S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
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fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
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# Import cleaning and merge functions
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exec(open('0_Cleaning_and_merge_functions.py').read())
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# Output folder
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BUCKET_OUT = "projet-bdc2324-team1"
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# Ignore warning
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warnings.filterwarnings('ignore')
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def export_dataset(df, output_name):
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print('Exportation of temporary dataset :', output_name)
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FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + output_name
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with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
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df.to_csv(file_out, index = False)
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## 1 - Cleaning of the datasets
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# Cleaning customerplus
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df1_customerplus_clean = preprocessing_customerplus(directory_path = "1")
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## Exportation
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export_dataset(df = df1_customerplus_clean, output_name = "0_Input/Company_1/customerplus_cleaned.csv")
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# Cleaning target area
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df1_target_information = preprocessing_target_area(directory_path = "1")
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## Exportation
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export_dataset(df = df1_campaigns_information, output_name = "0_Input/Company_1/Campaigns dataset clean.csv")
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# Cleaning campaign area
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df1_campaigns_information = preprocessing_campaigns_area(directory_path = "1")
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## Exportation
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export_dataset(df = df1_campaigns_information, output_name = "0_Input/Company_1/Campaigns dataset clean.csv")
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## Exportation
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export_dataset(df = df1_campaigns_information, output_name = "0_Temp/Company 1 - Campaigns dataset clean.csv")
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# Cleaning product area
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df1_products_purchased_reduced = uniform_product_df(directory_path = "1")
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## Exportation
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export_dataset(df = df1_campaigns_information, output_name = "0_Input/Company_1/Campaigns dataset clean.csv")
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#Exportation
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export_dataset(df = df1_products_purchased_reduced, output_name = "0_Temp/Company 1 - Purchases.csv")
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