Purpose:

The Python script is designed to clear and reset the ‘AUTOMATION - Outlier’ column in a data table to prepare it for further processing.

To Elaborate

The script addresses the need to reset a specific column, ‘AUTOMATION - Outlier’, in a data table to ensure it is ready for subsequent operations or analyses. This is particularly useful in scenarios where the column may contain outdated or irrelevant data that could interfere with new calculations or assessments. By clearing this column, the script ensures that any automated processes relying on this data start with a clean slate, thereby maintaining data integrity and accuracy in reporting or decision-making processes.

Walking Through the Code

1. Data Preparation
   • The script begins by defining the primary data source and relevant column names from a dictionary object, dataSourceDict, which contains the data table.
   • It specifies the columns related to reporting, such as ‘Group’, ‘Publisher’, ‘Account’, ‘Campaign’, and ‘Group ID’.
2. Output Initialization
   • The script initializes the output DataFrame, outputDf, by copying the input DataFrame, inputDf, to avoid altering the original data.
   • It sets the ‘AUTOMATION - Outlier’ column in the output DataFrame to an empty string, effectively clearing any existing data in this column.
3. Data Display
   • Finally, the script prints the first few rows of the modified DataFrame using the tableize function to provide a preview of the cleared ‘AUTOMATION - Outlier’ column.

Vitals

• Script ID : 1127
• Client ID / Customer ID: 1306927027 / 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 17:28
• Last Updated by dwaidhas@marinsoftware.com on 2024-05-22 17:29

> 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