Purpose

Tags dimension as brand or non-brand.

To Elaborate

The Python script solves the problem of tagging a dimension as either brand or non-brand. It checks if a specific column in the input DataFrame contains the word “Brand” in the campaign name and creates two separate DataFrames for brand and non-brand tagging. It then merges these two DataFrames and outputs the result.

Walking Through the Code

1. The script defines column constants for the campaign, account, and brand/non-brand columns.
2. It checks if the campaign column contains the word “Brand” using the str.contains() method.
3. Based on the check, it creates two separate DataFrames: one for brand tagging and one for non-brand tagging.
4. It sets the brand/non-brand column value to “Brand” for the brand DataFrame and “Non-Brand” for the non-brand DataFrame.
5. The script merges the brand and non-brand DataFrames using the merge() method with an outer join.
6. It prints the tableized version of the output DataFrame using the tableize() function.

Vitals

• Script ID : 133
• Client ID / Customer ID: 1306922587 / 2
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Brand/Non-Brand
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2023-05-25 05:51
• Last Updated by Byron Porter on 2023-12-07 21:03

> See it in Action

Python Code

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RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_B_NB = 'Brand/Non-Brand'

# Check if RPT_COL_CAMPAIGN column has 'Brand' in the campaign name
bcheck = (inputDf[RPT_COL_CAMPAIGN].str.contains('Brand', case=False, na=False))

# Use check to create two DataFrames. One for Brand and one for Non-Brand tagging
brandDf = inputDf.loc[bcheck, [BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_B_NB]]
brandDf.loc[:, BULK_COL_B_NB] = 'Brand'

nonbrandDf = inputDf.loc[~bcheck, [BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_B_NB]]
nonbrandDf.loc[:, BULK_COL_B_NB] = 'Non-Brand' 

# Merges the Brand and Non-Brand dataframes
outputDf = brandDf.merge(nonbrandDf, how='outer')

# Print the tableized version of the output DataFrame
print(tableize(outputDf))

Post generated on 2024-05-15 07:44:05 GMT