started assets

prepared_steps/2/LocationScript.py

@@ -6,6 +6,7 @@ country_mapping = {
 
 # Read the input CSV file, assuming the second row is the header
 read_df = pd.read_csv('../1/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False)
+read_df_ib = pd.read_csv('../1/SCInstalledBase__c.csv', header=0, keep_default_na=False)
 for row in read_df.to_dict('records'):
     try:
         # Your processing logic here
@@ -15,14 +16,16 @@ for row in read_df.to_dict('records'):
 
 # Columns for reindexing
 reindex_columns = ['City__c','Country__c','Extension__c','FlatNo__c','Floor__c','GeoX__c','GeoY__c','HouseNo__c','Id','PostalCode__c','Street__c']
+# ArticleNo__c,CommissioningDate__c,Id,InstallationDate__c,InstalledBaseLocation__c,InstalledBaseLocation__r.Id,Name,ProductEnergy__c,ProductUnitClass__c,ProductUnitType__c,SerialNo__c,SerialNoException__c
+reindex_columns_ib = ['ArticleNo__c','CommissioningDate__c','Id','InstallationDate__c','InstalledBaseLocation__c','InstalledBaseLocation__r.Id','Name','ProductEnergy__c','ProductUnitClass__c','ProductUnitType__c','SerialNo__c','SerialNoException__c']
 
 # Reindex the columns to match the desired format
 df = read_df.reindex(reindex_columns, axis=1)
+df_ib = read_df_ib.reindex(reindex_columns_ib, axis=1)
 
 df['Street'] = (
     df['Street__c'].astype(str) + ' ' +
-    df['HouseNo__c'].astype(str) + ' ' +
-    df['Extension__c'].astype(str)
+    df['HouseNo__c'].astype(str)
 )
 
 # Remove any trailing spaces that may result from missing values
@@ -35,6 +38,21 @@ df['PKey__c'] = (
     df['Country__c'].astype(str)
 )
 
+# Merge df_ib with df including additional columns
+merged_df_ib = pd.merge(df_ib, 
+                        df[['Id', 'PKey__c', 'Extension__c', 'FlatNo__c', 'Floor__c']], 
+                        left_on='InstalledBaseLocation__c',
+                        right_on='Id',
+                        how='left')
+
+# Handle missing values by setting them to None
+merged_df_ib['Extension__c'] = merged_df_ib['Extension__c'].fillna('')
+merged_df_ib['FlatNo__c'] = merged_df_ib['FlatNo__c'].fillna('')
+merged_df_ib['Floor__c'] = merged_df_ib['Floor__c'].fillna('')
+
+# If there are missing values (no match found), you can fill them with a placeholder
+merged_df_ib['PKey__c'].fillna('Not Found', inplace=True)
+
 ## 1. Address.csv
 # Columns needed for Address table based on the input CSV structure
 address_columns = ['City__c', 'Country__c', 
@@ -85,18 +103,22 @@ child_columns = ['Extension__c', 'FlatNo__c', 'Floor__c', 'City__c', 'Country__c
 # Modify child_df by explicitly creating a new DataFrame
 child_df = df[child_columns].copy()  # Add .copy() to create an explicit copy
 
-# Now create the 'Name' column without any warnings
+# Create the 'Name' column with simplified logic
 child_df['Name'] = (
+    # Check if all three fields are not null; if so, concatenate them
     child_df['Floor__c'].astype(str) + '-' +
     child_df['FlatNo__c'].astype(str) + '-' +
     child_df['Extension__c'].astype(str)
 )
 
+# Replace any row where 'Floor__c', 'FlatNo__c', and 'Extension__c' are all empty with "HOME"
+child_df.replace({'Name': {'--': 'HOME'}}, inplace=True)
+
 # Rename columns to match the desired format
-child_df.columns = ['Extension', 'Flat', 'Floor', 'City', 'Country',
+child_df.columns = ['Extension__c', 'Flat__c', 'Floor__c', 'City', 'Country',
                      'PostalCode', 'Street', 'PKey__c', 'Name']
 
-child_df = child_df.drop_duplicates(subset=['Extension', 'Flat', 'Floor','City', 'Country', 'PostalCode', 'Street'], keep='first')
+child_df = child_df.drop_duplicates(subset=['Extension__c', 'Flat__c', 'Floor__c','City', 'Country', 'PostalCode', 'Street'], keep='first')
 
 child_df = child_df.drop('Country', axis=1)
 child_df = child_df.drop('PostalCode', axis=1)
@@ -108,10 +130,19 @@ child_df['IsInventoryLocation'] = 'false'
 child_df['IsMobile'] = 'false'
 child_df['LocationType'] = 'Site'
 
+## 4. Assets.csv
+#ArticleNo__c,CommissioningDate__c,Id,InstallationDate__c,InstalledBaseLocation__c,InstalledBaseLocation__r.Extension__c,InstalledBaseLocation__r.FlatNo__c,InstalledBaseLocation__r.Floor__c,InstalledBaseLocation__r.Id,Name,ProductEnergy__c,ProductUnitClass__c,ProductUnitType__c,SerialNo__c,SerialNoException__c
+
+merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__c', axis=1)
+merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__r.Id', axis=1)
+merged_df_ib = merged_df_ib.drop('Id_y', axis=1)
+print(merged_df_ib.columns)
+merged_df_ib.columns = ['Product2.EAN_Product_Code__c', 'FSL_1st_Ignition_Date__c', 'Id', 'InstallDate',  'Name', 'Kind_of_Energy__c', 'Kind_of_Installation__c', 'Main_Product_Group__c', 'SerialNumber', 'Serialnumber_Exception__c', 'Location.PKey__c', 'Location.Extension__c', 'Location.Flat__c', 'Location.Floor__c',] 
 
 # Write each DataFrame to a separate CSV file
 address_df.to_csv('../3/Address.csv', index=False)
 parent_df.to_csv('../3/Location.csv', index=False)
 child_df.to_csv('../5/Location.csv', index=False)
+merged_df_ib.to_csv('../7/Asset.csv', index=False)
 
 print('Data has been successfully split into Address.csv, Parent_Location.csv, and Child_Location.csv files with duplicate checks applied.')