Purpose:

The Python script is designed to clear the ‘AUTOMATION - Outlier’ column in a dataset, ensuring it is reset to an empty state.

To Elaborate

The script addresses the need to reset or clear a specific column, ‘AUTOMATION - Outlier’, in a dataset used for structured budget allocation (SBA) processes. This column may contain data that needs to be removed or reset before further processing or analysis. By clearing this column, the script ensures that any previous outlier data does not interfere with new data processing tasks. This is particularly useful in scenarios where data integrity and accuracy are crucial, such as financial reporting or automated decision-making systems. The script operates on a copy of the original dataset to preserve the integrity of the initial data.

Walking Through the Code

1. Data Preparation
   • The script begins by defining the primary data source and relevant columns from a dictionary named dataSourceDict.
   • It creates a copy of the input DataFrame (inputDf) to ensure the original data remains unchanged during processing.
2. Column Clearing
   • The script specifically targets the ‘AUTOMATION - Outlier’ column in the copied DataFrame (outputDf).
   • It sets this column to an empty string, effectively clearing any existing data.
3. Output Display
   • Finally, the script prints the first few rows of the modified DataFrame using the tableize function, allowing users to verify that the ‘AUTOMATION - Outlier’ column has been successfully cleared.

Vitals

• Script ID : 1131
• Client ID / Customer ID: 1306927029 / 60270153
• Action Type: Bulk Upload
• Item Changed: AdGroup
• Output Columns: Account, Campaign, Group, AUTOMATION - Outlier
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
• Created by dwaidhas@marinsoftware.com on 2024-05-22 18:26
• Last Updated by dwaidhas@marinsoftware.com on 2024-05-22 18:26

> See it in Action

Python Code

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##
## name: Clear Automation - Outlier
## description:
##  
## 
## author: Dana Waidhas 
## created: 2024-05-22
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_GROUP = 'Group'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP_ID = 'Group ID'
RPT_COL_AUTOMATION__OUTLIER = 'AUTOMATION - Outlier'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_AUTOMATION__OUTLIER = 'AUTOMATION - Outlier'
outputDf[BULK_COL_AUTOMATION__OUTLIER] = "<<YOUR VALUE>>"

# user code start here
outputDf = inputDf.copy()  # Make a copy to avoid modifying the original DataFrame
outputDf[BULK_COL_AUTOMATION__OUTLIER] = ""  # Clear the 'AUTOMATION INFO' column

# user code start here
print(tableize(outputDf.head()))

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