Purpose:

The Python script updates the ‘Amazon Portfolio’ column in a DataFrame to match the ‘Portfolio’ column when discrepancies or null values are found.

To Elaborate

The script is designed to ensure consistency between two columns in a DataFrame: ‘Portfolio’ and ‘Amazon Portfolio’. It checks each row to see if the ‘Amazon Portfolio’ value is different from the ‘Portfolio’ value or if it is null or blank. If any of these conditions are met, the script updates the ‘Amazon Portfolio’ to match the ‘Portfolio’. This process helps maintain data integrity by aligning the ‘Amazon Portfolio’ with the ‘Portfolio’ wherever discrepancies or missing values are detected. The script then filters and returns only the rows where changes have been made, allowing users to focus on the updated data.

Walking Through the Code

1. Initialization:
   • The script begins by defining a function named process that takes an input DataFrame (inputDf) as its parameter.
   • It creates a copy of the input DataFrame named outputDf to work with, preserving the original data.
2. Iteration and Update:
   • The script iterates through each row of the outputDf using a loop.
   • For each row, it checks if the ‘Amazon Portfolio’ is different from ‘Portfolio’, or if it is null or blank.
   • If any of these conditions are true, it updates the ‘Amazon Portfolio’ to match the ‘Portfolio’.
3. Filtering Changes:
   • After updating, the script filters the DataFrame to identify rows where the ‘Amazon Portfolio’ has been changed.
   • It uses conditions to check for discrepancies or null values that have been corrected.
4. Output:
   • The function returns the filtered DataFrame containing only the rows where changes were made, allowing users to review the updates.

Vitals

• Script ID : 367
• Client ID / Customer ID: 1306926773 / 50395
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Amazon Portfolio
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jeremy Brown (jbrown@marinsoftware.com)
• Created by Jeremy Brown on 2023-10-17 11:10
• Last Updated by Jeremy Brown on 2023-12-06 04:01

> See it in Action

Python Code

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#def process(inputDf):
    # Create output DataFrame with the desired columns
#    outputDf = pd.DataFrame(columns=["Campaign", "Account", "Publisher", "Portfolio", "Amazon Portfolio"])

# Define the process function
def process(inputDf):
    # Create a copy of the input DataFrame to work with
    outputDf = inputDf.copy()
    print(tableize(inputDf))
    # Iterate through each row in the DataFrame
    for index, row in outputDf.iterrows():
        portfolio = row["Portfolio"]
        amazon_portfolio = row["Amazon Portfolio"]

        # Check if Amazon Portfolio is different from Portfolio or is null/blank, and if so, update it
        if amazon_portfolio != portfolio or pd.isna(amazon_portfolio) or amazon_portfolio.strip() == "":
            outputDf.at[index, "Amazon Portfolio"] = portfolio

    # Filter the changed rows
    # changed_rows = outputDf[outputDf["Amazon Portfolio"] != inputDf["Amazon Portfolio"]]
    changed_rows = outputDf[(outputDf["Amazon Portfolio"] != inputDf["Amazon Portfolio"]) | (inputDf["Amazon Portfolio"].isnull() & outputDf["Amazon Portfolio"].notnull())]

    # Print the changed data for debugging

    return changed_rows

# Trigger the main process
outputDf = process(inputDf)

Post generated on 2025-03-11 01:25:51 GMT