Purpose

The script tags campaigns as ‘Brand’ or ‘Non-Brand’ based on the presence of the word ‘Brand’ in the campaign name.

To Elaborate

The Python script is designed to categorize marketing campaigns into ‘Brand’ or ‘Non-Brand’ categories. This categorization is based on the presence of the word ‘Brand’ within the campaign name. The script processes a dataset of campaigns, identifies those that include ‘Brand’ in their names, and tags them accordingly. Campaigns without the word ‘Brand’ are tagged as ‘Non-Brand’. This automated tagging helps in organizing and analyzing marketing data, ensuring that campaigns are correctly classified for reporting and strategic decision-making purposes.

Walking Through the Code

1. Initial Setup
   The script begins by defining constants for column names used in the input and output dataframes. These constants help in maintaining consistency and readability throughout the script.

2. Brand Check
   A boolean check (bcheck) is performed on the campaign names to determine if they contain the word ‘Brand’. This check is case-sensitive and ignores any missing values.

3. DataFrame Creation
   • Two separate dataframes are created based on the bcheck result:
     • brandDf for campaigns identified as ‘Brand’.
     • nonbrandDf for campaigns identified as ‘Non-Brand’.
   • Each dataframe includes columns for account, campaign, and the brand/non-brand tag.
4. Tagging
   • The brandDf dataframe is tagged with ‘Brand’.
   • The nonbrandDf dataframe is tagged with ‘Non-Brand’.

5. Merging DataFrames
   The script merges the brandDf and nonbrandDf dataframes into a single outputDf using an outer join, ensuring all campaigns are included in the final output.

6. Output
   The final tagged dataframe is printed in a table format for easy visualization and further analysis.

Vitals

• Script ID : 135
• Client ID / Customer ID: 1306924501 / 60269325
• Action Type: Bulk Upload
• 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 06:29
• Last Updated by lneels@marinsoftware.com on 2023-12-06 04:01

> See it in Action

Python Code

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#
# Bran/Non-Brand Dimension Tagging
#
# Byron Porter
# 2023-05-25
#


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=True, 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 2024-11-27 06:58:46 GMT