finished pricebook2 & pricebookentry

.gitignore

@@ -15,3 +15,5 @@ Asset.csv
 venv/
 *Location.csv
 Product2.csv
+Pricebook2.csv
+PricebookEntry.csv

README.md

@@ -1,15 +1,16 @@
-# FSL Migration from Legacy with SFDMU & Python (Pandas)
+# FSL Migration from Legacy with SFDMU & Python
 
 This git is representing a migration from Legacy GMS solution to FSL Standard
 
 ## Installation
 
 Install Python [python](https://www.python.org/) to install python 3.13+.
-After installation open a terminal/powershell to install pandas globally
+After installation open a terminal/powershell to install the packages globally
 
 
 ```bash
-python -m pip install pandas
+cd ./migration_via_sfdmu-main
+python -m pip install -r ./requirements.txt
 ```
 
 ## Usage

prepared_steps/10_upsert_associated_location/AssociatedLocation_test.csv

@@ -0,0 +1,3 @@
+Type,ActiveFrom,ActiveTo,ParentRecordId,PKey__c,LocationId
+Installer (installation),2018-03-27,,0019Z00000fmCAYQA2,"Mr. P.J.M. Aalberselaan 19, 1181 XJ AMSTELVEEN, NL;;;",1319Z000000ij1RQAQ
+Owner,2018-04-06,,0019Z00000fmCAYQA2,"Mr. P.J.M. Aalberselaan 19, 1181 XJ AMSTELVEEN, NL;;;",1319Z000000ij1RQAQ

prepared_steps/11_insert_pricebook2_and_pricebookentries/command.txt

@@ -0,0 +1 @@
+sf sfdmu run --sourceusername csvfile --targetusername rene.kasseboehmer@vaillant.de.devrene

prepared_steps/11_insert_pricebook2_and_pricebookentries/export.json

@@ -0,0 +1,23 @@
+{
+    "allOrNone": true,
+    "excludeIdsFromCSVFiles": true,
+    "objects": [
+        {
+            "query": "SELECT Id, ProductCode FROM Product2",
+            "operation": "Readonly",
+            "master": false,
+            "externalId": "ProductCode"
+        },{
+            "query": "SELECT Business_Type__c, IsStandard, IsActive, Name, Brand__c, Country__c FROM Pricebook2",
+            "operation": "Insert",
+            "externalId": "Name",
+            "master": true,
+            "useSourceCSVFile": true
+        },{
+            "query": "SELECT IsActive, Product2Id, UnitPrice, Pricebook2Id FROM PricebookEntry",
+            "operation": "Insert",
+            "master": true,
+            "useSourceCSVFile": true
+        }
+    ]
+}

prepared_steps/12_insert_/command.txt

@@ -0,0 +1 @@
+sf sfdmu run --sourceusername csvfile --targetusername rene.kasseboehmer@vaillant.de.devrene

prepared_steps/12_insert_/export.json

@@ -0,0 +1,10 @@
+{
+    "allOrNone": true,
+    "excludeIdsFromCSVFiles": true,
+    "objects": [
+        {
+            "query": "SELECT Business_Type__c, IsStandard, IsActive, Name, Brand__c, Country__c FROM Pricebook2",
+            "operation": "Insert"
+        }
+    ]
+}

prepared_steps/1_extract_data/extract_via_simple_salesforce.py

@@ -152,6 +152,8 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Extract Salesforce data via Bulk API')
     parser.add_argument('--context', type=str, required=True,
                        help='Context name (e.g., "qa2", "prod")')
+    parser.add_argument('--country', type=str, required=True,
+                       help='CountryCode (e.g., "NL", "UK")')
     parser.add_argument('--output_path', type=str, required=False,
                        help='./')
     parser.add_argument('--sobjects', type=str, required=False,
@@ -170,7 +172,7 @@ if __name__ == '__main__':
     
     for query_def in queries:
         sobject = query_def['sobject']
-        query_str = query_def['query']
+        query_str = query_def['query'].replace('{country}', args.country)
         
         # Skip if --sobjects is provided and current sobject isn't in the list
         if selected_sobjects and sobject not in selected_sobjects:

prepared_steps/1_extract_data/queries.json

@@ -2,28 +2,34 @@
     [
         {
             "sobject": "SCInstalledBaseLocation__c",
-            "query": "SELECT Id, City__c, Country__c, GeoY__c, GeoX__c, PostalCode__c, Street__c, Extension__c, HouseNo__c, FlatNo__c, Floor__c FROM SCInstalledBaseLocation__c WHERE Country__c = 'NL' AND Id = 'a1B1r0000099EsfEAE' limit 1"
+            "query": "SELECT Id, City__c, Country__c, GeoY__c, GeoX__c, PostalCode__c, Street__c, Extension__c, HouseNo__c, FlatNo__c, Floor__c FROM SCInstalledBaseLocation__c WHERE Country__c = '{country}' AND Id = 'a1B1r0000099EsfEAE' limit 1"
         },{
             "sobject": "SCInstalledBase__c",
-            "query": "SELECT Id, Name, CommissioningDate__c,InstallationDate__c,ProductEnergy__c, ProductUnitClass__c,ArticleNo__c,SerialNo__c, SerialNoException__c, ProductUnitType__c, InstalledBaseLocation__c  FROM SCInstalledBase__c WHERE Country__c = 'NL' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
+            "query": "SELECT Id, Name, CommissioningDate__c,InstallationDate__c,ProductEnergy__c, ProductUnitClass__c,ArticleNo__c,SerialNo__c, SerialNoException__c, ProductUnitType__c, InstalledBaseLocation__c  FROM SCInstalledBase__c WHERE Country__c = '{country}' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
         },{
             "sobject": "Asset",
-            "query": "SELECT Id, Serialnumber FROM Asset WHERE Location.ParentLocation.Name LIKE '%NL'"
+            "query": "SELECT Id, Serialnumber FROM Asset WHERE Location.ParentLocation.Name LIKE '%{country}'"
         },{
             "sobject": "Address",
-            "query": "SELECT Id, Country, CountryCode, Street, City, ParentId, PostalCode FROM Address WHERE CountryCode = 'NL'"
+            "query": "SELECT Id, Country, CountryCode, Street, City, ParentId, PostalCode FROM Address WHERE CountryCode = '{country}'"
         },{
             "sobject": "ParentLocation",
-            "query": "SELECT Id, Name FROM Location WHERE Name LIKE '%NL'"
+            "query": "SELECT Id, Name FROM Location WHERE Name LIKE '%{country}'"
         },{
             "sobject": "ChildLocation",
-            "query": "SELECT Id, ParentLocationId, Name FROM Location WHERE ParentLocation.Name LIKE '%NL'"
+            "query": "SELECT Id, ParentLocationId, Name FROM Location WHERE ParentLocation.Name LIKE '%{country}'"
         },{
             "sobject": "Product2",
             "query": "SELECT Id, Main_Product_Group__c, Family, MaterialType__c, Name, Product_Code__c, ProductCode, EAN_Product_Code__c FROM Product2"
         },{
             "sobject": "SCInstalledBaseRole__c",
-            "query": "SELECT Id, InstalledBaseLocation__c, Role__c, ValidFrom__c, ValidTo__c, Account__c  FROM SCInstalledBaseRole__c WHERE InstalledBaseLocation__r.Country__c = 'NL' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
+            "query": "SELECT Id, InstalledBaseLocation__c, Role__c, ValidFrom__c, ValidTo__c, Account__c  FROM SCInstalledBaseRole__c WHERE InstalledBaseLocation__r.Country__c = '{country}' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
+        },{
+            "sobject": "SCPriceList__c",
+            "query": "SELECT Id, Name, Brand__r.Name, Country__c from SCPriceList__c WHERE Country__c = '{country}' AND PriceList__c != null"
+        },{
+            "sobject": "SCPriceListItem__c",
+            "query": "SELECT Id, Article__r.Name, Price__c, PriceUnit__c, Pricelist__c, ValidFrom__c, ValidTo__c, Article__r.EANCode__c, Pricelist__r.Brand__r.Name, Pricelist__r.Country__c  FROM SCPriceListItem__c WHERE Country__c = '{country}' AND PriceList__c != null AND (ValidTo__c >= TODAY OR ValidFrom__c >= TODAY) AND Article__c = 'a0Gw000001R9slpEAB'"
         }
     ]
 }

prepared_steps/1_extract_data/results/SCPriceListItem__c.csv

@@ -0,0 +1,2 @@
+"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"
+"a0p6900000eZ3nbAAC","0010021907","4024074807507","2011.85","1.0","a0q20000000PO8OAAW","2021-01-01","2099-12-31","Vaillant","NL"

prepared_steps/1_extract_data/results/SCPriceList__c.csv

@@ -0,0 +1,3 @@
+"Id","Name","Brand__r.Name","Country__c"
+"a0q20000000PO8OAAW","NL Vaillant","Vaillant","NL"
+"a0q20000000PO8TAAW","NL AWB","AWB","NL"

prepared_steps/2_transform_via_script/TransformScript.py

@@ -10,6 +10,8 @@ read_df = pd.read_csv('../1_extract_data/results/SCInstalledBaseLocation__c.csv'
 read_df_ib = pd.read_csv('../1_extract_data/results/SCInstalledBase__c.csv', header=0, keep_default_na=False, dtype=str)
 read_df_product2 = pd.read_csv('../1_extract_data/results/Product2.csv', header=0, keep_default_na=False, dtype=str)
 read_df_ibr = pd.read_csv('../1_extract_data/results/SCInstalledBaseRole__c.csv', header=0, keep_default_na=False, dtype=str)
+read_df_pricelist = pd.read_csv('../1_extract_data/results/SCPriceList__c.csv', header=0, keep_default_na=False, dtype=str)
+read_df_pricelistitem = pd.read_csv('../1_extract_data/results/SCPriceListItem__c.csv', header=0, keep_default_na=False, dtype=str)
 
 for row in read_df.to_dict('records'):
     try:
@@ -31,13 +33,19 @@ reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType_
 #reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
 #"Id","InstalledBaseLocation__c","Role__c","ValidFrom__c","ValidTo__c","Account__c"
 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 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_product2 = read_df_product2.reindex(reindex_columns_product2, axis=1)
 df_ibr = read_df_ibr.reindex(reindex_columns_ibr, axis=1)
+df_pricelist = read_df_pricelist.reindex(reindex_columns_pricelist, axis=1)
+df_pricelistitem = read_df_pricelistitem.reindex(reindex_columns_pricelistitem, axis=1)
 
+#creating street column
+# Concatenate 'Street__c' and 'HouseNo__c' to create the 'Street' column
 df['Street'] = (
     df['Street__c'].astype(str) + ' ' +
     df['HouseNo__c'].astype(str)
@@ -246,14 +254,55 @@ for index, row in tqdm(merged_df_ib.iterrows(), total=len(merged_df_ib)):
         if product_group not in valid_groups:
             merged_df_ib.loc[index, 'Kind_of_Energy__c'] = None  # or set to empty string
 
-print(merged_df_ib)
+
+
+# Pricelist to Pricebook2
+columns_pricebook2 = ['Id', 'Name', 'Brand__c', 'Country__c']
+
+df_pricelist.columns = columns_pricebook2
+
+df_pricelist.insert(0, 'IsActive', 'true')
+df_pricelist.insert(0, 'IsStandard', 'false')
+df_pricelist.insert(0, 'Business_Type__c', 'Service')
+
+df_pricelist['Name'] = (
+    df_pricelist['Country__c'].astype(str).fillna('').str.upper() + ' ' +
+    df_pricelist['Brand__c'].astype(str).fillna('').str.upper() + ' ' +
+    df_pricelist['Business_Type__c'].astype(str).fillna('').str.upper()
+)
+
+df_pricelist = df_pricelist.drop('Id', axis=1)
+
+df_pricelistitem['Pricebook2.Name'] = (
+    df_pricelistitem['Pricelist__r.Country__c'].astype(str).fillna('').str.upper() + ' ' + 
+    df_pricelistitem['Pricelist__r.Brand__r.Name'].astype(str).fillna('').str.upper() + ' ' + 
+    'SERVICE'
+)
+
+df_pricelistitem = df_pricelistitem.drop('Id', axis=1)
+df_pricelistitem = df_pricelistitem.drop('PriceUnit__c', axis=1)
+df_pricelistitem = df_pricelistitem.drop('Pricelist__c', axis=1)
+df_pricelistitem = df_pricelistitem.drop('ValidFrom__c', axis=1)
+df_pricelistitem = df_pricelistitem.drop('ValidTo__c', axis=1)
+df_pricelistitem = df_pricelistitem.drop('Pricelist__r.Country__c', axis=1)
+df_pricelistitem = df_pricelistitem.drop('Pricelist__r.Brand__r.Name', axis=1)
+
+df_pricelistitem.insert(0, 'IsActive', 'true')
+
+print(df_pricelistitem)
+
+columns_pricebookentry = ['IsActive', 'Product2.Product_Code__c', 'Product2.EAN_Product_Code__c', 'UnitPrice', 'Pricebook2.Name']
+
+df_pricelistitem.columns = columns_pricebookentry
 
 # Write each DataFrame to a separate CSV file
 address_df.to_csv('../3_upsert_address_and_parent_location/Address.csv', index=False)
 parent_df.to_csv('../3_upsert_address_and_parent_location/Location.csv', index=False)
 child_df.to_csv('../5_upsert_child_location/Location.csv', index=False)
 merged_df_ib.to_csv('../7_upsert_assets/Asset.csv', index=False)
-df_ibr.to_csv('../9_upsert_associated_location/AssociatedLocation.csv', index=False)
+df_ibr.to_csv('../10_upsert_associated_location/AssociatedLocation.csv', index=False)
+df_pricelist.to_csv('../11_insert_pricebook2/Pricebook2.csv', index=False)
+df_pricelistitem.to_csv('../11_insert_pricebook2/PricebookEntry.csv', index=False)
 
 ## end mapping
 

prepared_steps/2_transform_via_script/command.txt

@@ -1 +1 @@
-python .\LocationScript.py
+python .\TransformScript.py