finished pricebook2 & pricebookentry
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@ -10,6 +10,8 @@ read_df = pd.read_csv('../1_extract_data/results/SCInstalledBaseLocation__c.csv'
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read_df_ib = pd.read_csv('../1_extract_data/results/SCInstalledBase__c.csv', header=0, keep_default_na=False, dtype=str)
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read_df_product2 = pd.read_csv('../1_extract_data/results/Product2.csv', header=0, keep_default_na=False, dtype=str)
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read_df_ibr = pd.read_csv('../1_extract_data/results/SCInstalledBaseRole__c.csv', header=0, keep_default_na=False, dtype=str)
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read_df_pricelist = pd.read_csv('../1_extract_data/results/SCPriceList__c.csv', header=0, keep_default_na=False, dtype=str)
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read_df_pricelistitem = pd.read_csv('../1_extract_data/results/SCPriceListItem__c.csv', header=0, keep_default_na=False, dtype=str)
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for row in read_df.to_dict('records'):
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try:
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@ -31,13 +33,19 @@ reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType_
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#reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
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#"Id","InstalledBaseLocation__c","Role__c","ValidFrom__c","ValidTo__c","Account__c"
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reindex_columns_ibr = ['Id', 'InstalledBaseLocation__c', 'Role__c', 'ValidFrom__c', 'ValidTo__c', 'Account__c']
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reindex_columns_pricelist = ['Id', 'Name', 'Brand__r.Name', 'Country__c']
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reindex_columns_pricelistitem = ['Id', 'Article__r.Name', 'Article__r.EANCode__c', 'Price__c', 'PriceUnit__c', 'Pricelist__c', 'ValidFrom__c', 'ValidTo__c', 'Pricelist__r.Brand__r.Name', 'Pricelist__r.Country__c']
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# Reindex the columns to match the desired format
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df = read_df.reindex(reindex_columns, axis=1)
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df_ib = read_df_ib.reindex(reindex_columns_ib, axis=1)
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df_product2 = read_df_product2.reindex(reindex_columns_product2, axis=1)
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df_ibr = read_df_ibr.reindex(reindex_columns_ibr, axis=1)
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df_pricelist = read_df_pricelist.reindex(reindex_columns_pricelist, axis=1)
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df_pricelistitem = read_df_pricelistitem.reindex(reindex_columns_pricelistitem, axis=1)
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#creating street column
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# Concatenate 'Street__c' and 'HouseNo__c' to create the 'Street' column
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df['Street'] = (
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df['Street__c'].astype(str) + ' ' +
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df['HouseNo__c'].astype(str)
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@ -246,14 +254,55 @@ for index, row in tqdm(merged_df_ib.iterrows(), total=len(merged_df_ib)):
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if product_group not in valid_groups:
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merged_df_ib.loc[index, 'Kind_of_Energy__c'] = None # or set to empty string
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print(merged_df_ib)
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# Pricelist to Pricebook2
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columns_pricebook2 = ['Id', 'Name', 'Brand__c', 'Country__c']
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df_pricelist.columns = columns_pricebook2
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df_pricelist.insert(0, 'IsActive', 'true')
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df_pricelist.insert(0, 'IsStandard', 'false')
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df_pricelist.insert(0, 'Business_Type__c', 'Service')
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df_pricelist['Name'] = (
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df_pricelist['Country__c'].astype(str).fillna('').str.upper() + ' ' +
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df_pricelist['Brand__c'].astype(str).fillna('').str.upper() + ' ' +
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df_pricelist['Business_Type__c'].astype(str).fillna('').str.upper()
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)
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df_pricelist = df_pricelist.drop('Id', axis=1)
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df_pricelistitem['Pricebook2.Name'] = (
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df_pricelistitem['Pricelist__r.Country__c'].astype(str).fillna('').str.upper() + ' ' +
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df_pricelistitem['Pricelist__r.Brand__r.Name'].astype(str).fillna('').str.upper() + ' ' +
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'SERVICE'
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)
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df_pricelistitem = df_pricelistitem.drop('Id', axis=1)
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df_pricelistitem = df_pricelistitem.drop('PriceUnit__c', axis=1)
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df_pricelistitem = df_pricelistitem.drop('Pricelist__c', axis=1)
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df_pricelistitem = df_pricelistitem.drop('ValidFrom__c', axis=1)
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df_pricelistitem = df_pricelistitem.drop('ValidTo__c', axis=1)
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df_pricelistitem = df_pricelistitem.drop('Pricelist__r.Country__c', axis=1)
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df_pricelistitem = df_pricelistitem.drop('Pricelist__r.Brand__r.Name', axis=1)
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df_pricelistitem.insert(0, 'IsActive', 'true')
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print(df_pricelistitem)
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columns_pricebookentry = ['IsActive', 'Product2.Product_Code__c', 'Product2.EAN_Product_Code__c', 'UnitPrice', 'Pricebook2.Name']
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df_pricelistitem.columns = columns_pricebookentry
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# Write each DataFrame to a separate CSV file
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address_df.to_csv('../3_upsert_address_and_parent_location/Address.csv', index=False)
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parent_df.to_csv('../3_upsert_address_and_parent_location/Location.csv', index=False)
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child_df.to_csv('../5_upsert_child_location/Location.csv', index=False)
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merged_df_ib.to_csv('../7_upsert_assets/Asset.csv', index=False)
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df_ibr.to_csv('../9_upsert_associated_location/AssociatedLocation.csv', index=False)
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df_ibr.to_csv('../10_upsert_associated_location/AssociatedLocation.csv', index=False)
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df_pricelist.to_csv('../11_insert_pricebook2/Pricebook2.csv', index=False)
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df_pricelistitem.to_csv('../11_insert_pricebook2/PricebookEntry.csv', index=False)
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## end mapping
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@ -1 +1 @@
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python .\LocationScript.py
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python .\TransformScript.py
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