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data extract switch to simple_salesforce bulkv2

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Rene Kaßeböhmer
2025-04-09 11:21:08 +02:00
parent 2e6d82d9cc
commit 19eacdf2f5
4 changed files with 115 additions and 28 deletions
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19
prepared_steps/2_transform_via_script/LocationScript.py
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@ -5,9 +5,9 @@ country_mapping = {
}
# Read the input CSV file, assuming the second row is the header
read_df = pd.read_csv('../1_extract_data/target/SCInstalledBaseLocation__c_upsert_target.csv', header=0, keep_default_na=False, dtype=str)
read_df_ib = pd.read_csv('../1_extract_data/target/object-set-2/SCInstalledBase__c_upsert_target.csv', header=0, keep_default_na=False, dtype=str)
read_df_product2 = pd.read_csv('../1_extract_data/target/object-set-6/Product2_upsert_target.csv', header=0, keep_default_na=False, dtype=str)
read_df = pd.read_csv('../1_extract_data/results/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False, dtype=str)
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)
for row in read_df.to_dict('records'):
try:
@ -17,11 +17,16 @@ for row in read_df.to_dict('records'):
print(f'KeyError: {e}')
# 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']
#"Id","City__c","Country__c","GeoY__c","GeoX__c","PostalCode__c","Street__c","Extension__c","HouseNo__c","FlatNo__c","Floor__c"
reindex_columns = ['Id','City__c','Country__c','GeoY__c','GeoX__c','PostalCode__c','Street__c','Extension__c','HouseNo__c','FlatNo__c','Floor__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']
#"Id","Name","CommissioningDate__c","InstallationDate__c","ProductEnergy__c","ProductUnitClass__c","ArticleNo__c","SerialNo__c","SerialNoException__c","ProductUnitType__c","InstalledBaseLocation__c"
reindex_columns_ib = ['Id','Name','CommissioningDate__c','InstallationDate__c','ProductEnergy__c','ProductUnitClass__c','ArticleNo__c','SerialNo__c','SerialNoException__c','ProductUnitType__c','InstalledBaseLocation__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']
# EAN_Product_Code__c,Family,Id,Main_Product_Group__c,MaterialType__c,Name,Product_Code__c,ProductCode
reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
#"Id","Main_Product_Group__c","Family","MaterialType__c","Name","Product_Code__c","ProductCode","EAN_Product_Code__c"
reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType__c','Name','Product_Code__c','ProductCode','EAN_Product_Code__c']
#reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
# Reindex the columns to match the desired format
df = read_df.reindex(reindex_columns, axis=1)
@ -153,7 +158,7 @@ child_df['LocationType'] = 'Site'
#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('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.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.ExternalReference']