several fixes; Location Van, AssociatedLocation

prepared_steps/2_transform_via_script/TransformScript.py

@@ -42,7 +42,7 @@ reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType_
 reindex_columns_ibr = ['Id', 'InstalledBaseLocation__c', 'Role__c', 'ValidFrom__c', 'ValidTo__c', 'Account__c']
 reindex_columns_pricelist = ['Id', 'Name', 'Brand__r.Name', 'Country__c']
 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']
-reindex_columns_resourceassignment = ['Id', 'ValidTo__c', 'ValidFrom__c', 'Country__c', 'City__c', 'PostalCode__c', 'District__c', 'Street__c', 'HouseNo__c', 'Extension__c', 'FlatNo__c', 'Floor__c', 'GeoY__c', 'GeoX__c', 'Resource__c', 'Resource__r.Employee__r.Name', 'Stock__c']
+reindex_columns_resourceassignment = ['Id', 'ValidTo__c', 'ValidFrom__c', 'Country__c', 'City__c', 'PostalCode__c', 'District__c', 'Street__c', 'HouseNo__c', 'Extension__c', 'FlatNo__c', 'Floor__c', 'GeoY__c', 'GeoX__c', 'Resource__c', 'Resource__r.Employee__r.Name', 'Stock__c', 'Stock__r.ID2__c']
 reindex_columns_address_iot = ['Id', 'Country', 'CountryCode', 'Street', 'City', 'ParentId', 'PostalCode']
 reindex_columns_location_iot = ['Id', 'Name']
 
@@ -223,7 +223,7 @@ merged_df_ib = merged_df_ib.drop('Id_x', axis=1)
 merged_df_ib = merged_df_ib.drop('ProductUnitClass__c', axis=1)
 merged_df_ib = merged_df_ib.drop('ProductUnitType__c', axis=1)
 
-merged_df_ib.columns = ['Name', 'FSL_1st_Ignition_Date__c', 'InstallDate',  'Kind_of_Energy__c', 'Product2.Product_Code__c', 'SerialNumber', 'Serialnumber_Exception__c', 'Location.ExternalReference'] 
+merged_df_ib.columns = ['Name', 'FSL_1st_Ignition_Date__c', 'InstallDate',  'Kind_of_Energy__c', 'Product2.Product_Code__c', 'SerialNumber', 'Serialnumber_Exception__c', 'Location.PKey__c'] 
 #merged_df_ib = merged_df_ib.drop('Main_Product_Group__c', axis=1)
 # assign Main_Product_Group__c based on product2 records
 merged_df_ib = pd.merge(merged_df_ib, 
@@ -241,8 +241,11 @@ merged_df_ib = merged_df_ib.replace({'Kind_of_Energy__c': {'4': '3', '5': '3'}})
 
 ## 5. SCInstalledBaseRole__c.csv
 
+print(df_ibr)
+print(child_df)
+
 df_ibr = pd.merge(df_ibr, 
-                    child_df[['Id', 'ExternalReference']],
+                    child_df[['Id', 'PKey__c']],
                     left_on='InstalledBaseLocation__c',
                     right_on='Id',
                     how='left') 
@@ -253,9 +256,9 @@ df_ibr = df_ibr.drop('Id_x', axis=1)
 df_ibr = df_ibr.drop('Id_y', axis=1)
 df_ibr = df_ibr.drop('InstalledBaseLocation__c', axis=1)
 
-print(df_ibr.columns)
+print(df_ibr)
 
-df_ibr.columns = ['Type', 'ActiveFrom', 'ActiveTo', 'ParentRecordId', 'Location.ExternalReference']
+df_ibr.columns = ['Type', 'ActiveFrom', 'ActiveTo', 'ParentRecordId', 'Location.PKey__c']
 
 #remove kind_of_energy__c and kind_of_installation if field dependency to main product group is not correct
 # Create the mapping dictionary
@@ -345,7 +348,7 @@ df_pricelistitem.columns = columns_pricebookentry
 ##--------------------------------------------------------------------------##
 
 # Create van locations from resource assignments
-van_columns = ['Id', 'City__c', 'Country__c', 'Street__c', 'HouseNo__c', 'PostalCode__c', 'GeoY__c', 'GeoX__c', 'Resource__r.Employee__r.Name']
+van_columns = ['Id', 'City__c', 'Country__c', 'Street__c', 'HouseNo__c', 'PostalCode__c', 'GeoY__c', 'GeoX__c', 'Resource__r.Employee__r.Name', 'Stock__r.ID2__c']
 van_address_columns = ['City__c', 'Country__c', 'Street__c', 'HouseNo__c', 'PostalCode__c', 'GeoY__c', 'GeoX__c', 'ExternalReference']
 van_df = df_resourceassignment[van_columns].copy()
 
@@ -362,6 +365,20 @@ van_df['ExternalReference'] = (
     van_df['Country__c'].astype(str)
 )
 
+# Process Stock__r.ID2__c field
+def extract_last_4_digits(value):
+    if pd.isna(value) or not isinstance(value, str):
+        return '1000'
+    try:
+        last_4 = value[-4:]
+        if last_4.isdigit():
+            return last_4
+        return '1000'
+    except:
+        return '1000'
+
+van_df['Stock__r.ID2__c'] = van_df['Stock__r.ID2__c'].apply(extract_last_4_digits)
+
 # Add van records to child_df
 if not van_df.empty:
     van_records = pd.DataFrame({
@@ -371,10 +388,13 @@ if not van_df.empty:
         'DuplicateCheck__c': 'false',
         'IsInventoryLocation': 'true',
         'IsMobile': 'true',
-        'LocationType': 'Van'
+        'LocationType': 'Van',
+        'Location_Number__c': van_df['Stock__r.ID2__c']
     })
     child_df = pd.concat([child_df, van_records], ignore_index=True)
 
+child_df = child_df.drop('Id', axis=1)
+
 # Address and Parent Location for vans
 van_address_df = van_df[van_address_columns].copy()
 van_parenlocation_df = van_df[['City__c', 'Country__c', 'Street__c', 'PostalCode__c']].copy()
@@ -383,10 +403,7 @@ van_parenlocation_df = van_df[['City__c', 'Country__c', 'Street__c', 'PostalCode
 van_address_df['Country'] = van_address_df['Country__c'].map(country_mapping)
 van_address_df['Parent.Name'] = van_address_df['ExternalReference']
 van_address_df = van_address_df.drop('HouseNo__c', axis=1)
-print(van_address_df)
 van_address_df.columns = ['City', 'CountryCode', 'Street', 'PostalCode', 'Latitude', 'Longitude', 'PKey__c', 'Country', 'Parent.Name']
-print(van_address_df)
-
 
 # Only add addresses that don't already exist
 new_addresses = van_address_df[~van_address_df['PKey__c'].isin(address_df['PKey__c'])]
@@ -415,7 +432,7 @@ address_df.to_csv('../4_upsert_address_and_parent_location/Address.csv', index=F
 parent_df.to_csv('../4_upsert_address_and_parent_location/Location.csv', index=False)
 child_df.to_csv('../6_upsert_child_location/Location.csv', index=False)
 merged_df_ib.to_csv('../8_upsert_assets/Asset.csv', index=False)
-df_ibr.to_csv('../11_upsert_associated_location/AssociatedLocation.csv', index=False)
+df_ibr.to_csv('../11_upsert_associated_location/AssociatedLocation_beforetransform.csv', index=False)
 df_pricelist.to_csv('../12_insert_pricebook2_and_pricebookentries/Pricebook2.csv', index=False)
 df_pricelistitem.to_csv('../12_insert_pricebook2_and_pricebookentries/PricebookEntry.csv', index=False)
 merged_df_location_iot.to_csv('../3_update_address_and_location_data_for_migration/Location.csv', index=False)