Purpose:

The Python script categorizes campaigns into ‘Brand’ or ‘Non-Brand’ based on their names and outputs the results.

To Elaborate

The Python script is designed to classify marketing campaigns into two categories: ‘Brand’ and ‘Non-Brand’. It achieves this by examining the campaign names within a dataset to determine if they contain the word ‘Brand’. Campaigns that include ‘Brand’ are tagged as ‘Brand’, while those that do not are tagged as ‘Non-Brand’. This classification helps in organizing and analyzing marketing efforts based on their branding focus. The script processes the data by creating separate dataframes for each category and then merges them to provide a comprehensive view of the campaign classification.

Walking Through the Code

1. Initial Setup:
   • The script defines constants for column names used in the input and output dataframes.
   • It checks if the campaign names contain the word ‘Brand’, using a case-insensitive search.
2. DataFrame Creation:
   • Two separate dataframes are created based on the check: one for campaigns tagged as ‘Brand’ and another for those tagged as ‘Non-Brand’.
   • The ‘Brand/Non-Brand’ column is updated accordingly in each dataframe.
3. Merging DataFrames:
   • The script merges the ‘Brand’ and ‘Non-Brand’ dataframes using an outer join to ensure all campaigns are included in the final output.
4. Output:
   • The merged dataframe is printed in a table format, providing a clear view of the campaign categorization.

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 2025-03-11 01:25:51 GMT