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IB import nearly finished

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Rene Kaßeböhmer
2025-04-02 15:31:44 +02:00
parent 6bc3fd1a00
commit 5b5a2769c8
3 changed files with 30 additions and 43 deletions
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32
prepared_steps/2/LocationScript.py
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@ -5,8 +5,8 @@ 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)
read_df = pd.read_csv('../1/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False, dtype=str)
read_df_ib = pd.read_csv('../1/SCInstalledBase__c.csv', header=0, keep_default_na=False, dtype=str)
for row in read_df.to_dict('records'):
try:
# Your processing logic here
@ -45,14 +45,20 @@ merged_df_ib = pd.merge(df_ib,
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)
merged_df_ib['PKey__c'] = (
merged_df_ib['PKey__c'].astype(str) + ';' +
merged_df_ib['Extension__c'].astype(str) + ';' +
merged_df_ib['FlatNo__c'].astype(str) + ';' +
merged_df_ib['Floor__c'].astype(str)
)
merged_df_ib = merged_df_ib.drop('Extension__c', axis=1)
merged_df_ib = merged_df_ib.drop('FlatNo__c', axis=1)
merged_df_ib = merged_df_ib.drop('Floor__c', axis=1)
## 1. Address.csv
# Columns needed for Address table based on the input CSV structure
address_columns = ['City__c', 'Country__c',
@ -114,9 +120,17 @@ child_df['Name'] = (
# Replace any row where 'Floor__c', 'FlatNo__c', and 'Extension__c' are all empty with "HOME"
child_df.replace({'Name': {'--': 'HOME'}}, inplace=True)
# Create the 'ExternalReference' column for Asset assignment
child_df['ExternalReference'] = (
child_df['PKey__c'].astype(str) + ';' +
child_df['Extension__c'].astype(str) + ';' +
child_df['FlatNo__c'].astype(str) + ';' +
child_df['Floor__c'].astype(str)
)
# Rename columns to match the desired format
child_df.columns = ['Extension__c', 'Flat__c', 'Floor__c', 'City', 'Country',
'PostalCode', 'Street', 'PKey__c', 'Name']
'PostalCode', 'Street', 'PKey__c', 'Name', 'ExternalReference']
child_df = child_df.drop_duplicates(subset=['Extension__c', 'Flat__c', 'Floor__c','City', 'Country', 'PostalCode', 'Street'], keep='first')
@ -137,7 +151,7 @@ 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',]
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.ExternalReference']
# Write each DataFrame to a separate CSV file
address_df.to_csv('../3/Address.csv', index=False)