Script 1361: SCRIPT Assign Campaign Dimension Labels

Purpose:

The script assigns dimension labels to campaigns based on their naming conventions and campaign types.

To Elaborate

The Python script is designed to enhance campaign data by assigning specific dimension labels to each campaign based on its naming convention and type. This is particularly useful for marketing and advertising teams who need to categorize and analyze campaigns efficiently. The script processes a dataset containing campaign information, identifies key terms within campaign names, and assigns categories such as “Non-Branded,” “Student,” “Awareness,” or “Brand.” Additionally, it determines the device type (e.g., “Mobile” or “Desktop”) and targeting strategy (e.g., “Dynamic” or “Discovery”) based on the campaign’s name and type. This structured approach allows for consistent and automated categorization, facilitating better reporting and analysis.

Walking Through the Code

  1. Data Preparation:
    • The script begins by defining the primary data source and relevant columns from the input DataFrame, which includes client, campaign, account, and campaign type information.
    • An output DataFrame is initialized with specific columns to store the processed data.
  2. Processing Function:
    • A function named process is defined to handle the transformation of the input data.
    • It creates a copy of the necessary columns from the input DataFrame and converts the ‘Campaign’ column to lowercase to simplify string matching.
    • The function assigns values to the ‘Campaign_Category’ column based on specific keywords found in the campaign names, such as “artist,” “podcast,” and “brand.”
  3. Device and Targeting Assignment:
    • The script assigns values to the ‘Campaign_Device’ column based on keywords like “mobile” and “desktop.”
    • It sets the ‘Campaign_Targeting’ column using the ‘Campaign Type’ as a default, with specific overrides for keywords like “dsa” and “discovery.”
  4. Final Adjustments:
    • The ‘cdimcheck’ column is set to “YES” for all entries to indicate that the dimension check has been completed.
    • A temporary column used for processing is removed from the DataFrame.
  5. Testing and Execution:
    • A unit test function, test_process, is included to verify the functionality of the process function, although it assumes correct input data.
    • The script concludes by executing the process function to generate the output DataFrame with assigned labels.

Vitals

  • Script ID : 1361
  • Client ID / Customer ID: 247648668 / 13095968
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Campaign_Category, Campaign_Device, Campaign_Targeting, cdimcheck
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jeremy Brown (jbrown@marinsoftware.com)
  • Created by Jeremy Brown on 2024-08-29 15:41
  • Last Updated by Jeremy Brown on 2024-08-29 15:43
> See it in Action

Python Code

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## author: Jeremy Brown
## created: 2024-08-29
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CLIENT = 'Client'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CDIMCHECK = 'cdimcheck'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_CATEGORY = 'Campaign_Category'
RPT_COL_CAMPAIGN_TARGETING = 'Campaign_Targeting'
RPT_COL_CAMPAIGN_DEVICE = 'Campaign_Device'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_IMPR = 'Impr.'

# output columns and initial values
BULK_COL_CLIENT = 'Client'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CAMPAIGN_CATEGORY = 'Campaign_Category'
BULK_COL_CAMPAIGN_DEVICE = 'Campaign_Device'
BULK_COL_CAMPAIGN_TARGETING = 'Campaign_Targeting'
BULK_COL_CDIMCHECK = 'cdimcheck'
outputDf[BULK_COL_CAMPAIGN_CATEGORY] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CAMPAIGN_DEVICE] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CAMPAIGN_TARGETING] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CDIMCHECK] = "<<YOUR VALUE>>"

# Function to process the input DataFrame and populate the output DataFrame
def process(inputDf):
    # Make a copy of the relevant columns from the input DataFrame for the output DataFrame
    outputDf = inputDf[['Client', 'Campaign', 'Account', 'cdimcheck', 'Campaign_Category', 'Campaign_Targeting', 'Campaign_Device']].copy()

    # Convert the 'Campaign' column to lowercase for easier checking
    outputDf['campaign_lower'] = outputDf['Campaign'].str.lower()

    # Assign 'Campaign_Category' based on the campaign naming convention
    outputDf['Campaign_Category'] = ""
    outputDf.loc[outputDf['campaign_lower'].str.contains('artist'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('podcast'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('family'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('echo'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('content'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('student'), 'Campaign_Category'] = "Student"
    outputDf.loc[outputDf['campaign_lower'].str.contains('awareness'), 'Campaign_Category'] = "Awareness"
    outputDf.loc[outputDf['campaign_lower'].str.contains('competitor'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('generic'), 'Campaign_Category'] = "Non-Branded"
    outputDf.loc[outputDf['campaign_lower'].str.contains('brand'), 'Campaign_Category'] = "Brand"

    # Assign 'Campaign_Device' based on the campaign naming convention
    outputDf['Campaign_Device'] = ""
    outputDf.loc[outputDf['campaign_lower'].str.contains('mobile'), 'Campaign_Device'] = "Mobile"
    outputDf.loc[outputDf['campaign_lower'].str.contains('desktop'), 'Campaign_Device'] = "Desktop"

    # Assign 'Campaign_Targeting' based on 'Campaign Type' and specific campaign names
    outputDf['Campaign_Targeting'] = inputDf['Campaign Type']  # Default to 'Campaign Type' column
    outputDf.loc[outputDf['campaign_lower'].str.contains('dsa'), 'Campaign_Targeting'] = "Dynamic"
    outputDf.loc[outputDf['campaign_lower'].str.contains('discovery'), 'Campaign_Targeting'] = "Discovery"

    # Set 'cdimcheck' to "YES" for all entries
    outputDf['cdimcheck'] = "YES"

    # Drop the temporary 'campaign_lower' column
    outputDf.drop(columns=['campaign_lower'], inplace=True)

    # Print the data changed for debug friendly
    print("Data after processing:")
    print(outputDf)

    return outputDf


# Unit test function for process
def test_process():
    print("###UNITTEST START####")
    
    try:
        # Assuming the function is tested with correct input data here
        # If the test passes, print pass message
        print("####PASS####")
    except Exception as e:
        # If the test fails, print fail message
        print(f"####FAIL#### {e}")


# Trigger the main process
outputDf = process(inputDf)

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

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