Purpose:

Automates the population of utmcampaign and utmmedium dimensions based on campaign name and type.

To Elaborate

The Python script is designed to automate the process of populating two specific dimensions, utmcampaign and utmmedium, based on the values of campaign name and campaign type, respectively. This is particularly useful for marketing and analytics teams who need to track and categorize their campaigns efficiently. The script processes a dataset, filtering out rows where these dimensions are already populated, and then applies specific transformation rules to fill in the missing values. The utmcampaign dimension is derived by transforming the campaign name to lowercase and replacing spaces with plus signs. The utmmedium dimension is determined based on the campaign type, with predefined mappings for different types such as “Search”, “Display”, “Video”, “Discovery”, and “Performance Max”. This ensures that the data is consistent and ready for further analysis or reporting.

Walking Through the Code

1. Data Preparation
   • The script begins by defining the primary data source and relevant columns from the dataset.
   • It sets up constants for both input and output columns related to campaign and account information.
2. Function Definitions
   • A function set_utm_campaign is defined to transform the campaign name by replacing spaces with plus signs and converting it to lowercase.
   • Another function, set_utm_medium, determines the utmmedium value based on the campaign type, with specific mappings for different campaign types.
3. Data Filtering
   • The script filters the input data to exclude rows where both utmmedium and utmcampaign are already populated, creating a new dataframe filteredDf.
4. Data Transformation
   • The filtered data is copied to outputDf, which will store the final results.
   • The script applies the transformation functions to each row in the filtered dataframe, updating the utmcampaign and utmmedium columns in outputDf.
5. Output
   • Finally, the script prints the first few rows of the transformed dataframe to verify the results.

Vitals

• Script ID : 1341
• Client ID / Customer ID: 1306913420 / 60268008
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, utmcampaign, utmmedium
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Kyle Perkins (kyle.perkins@genesys.com)
• Created by Kyle Perkins on 2024-08-20 14:15
• Last Updated by Kyle Perkins on 2024-08-20 14:22

> See it in Action

Python Code

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##
## name: Campaign Dimension Update
## description:
##  Automates populating utmcampaign and utmmedium dimensions based on campaign name and campaign type values, respectively
## 
## author: Byron Porter
## created: 2024-07-24
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_UTMMEDIUM = 'utmmedium'
RPT_COL_UTMCAMPAIGN = 'utmcampaign'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_UTMCAMPAIGN = 'utmcampaign'
BULK_COL_UTMMEDIUM = 'utmmedium'

# define function to replace ' ' with '+' and convert to lowercase in Campaign value
def set_utm_campaign(campaign_name):
    return campaign_name.replace(' ', '+').lower()

# define function to determine utmmedium dimension value based on Campaign Type value
def set_utm_medium(campaign_type):
    if campaign_type == "Search":
        return "paidsearch"
    elif campaign_type in ["Display", "Video", "Discovery"]:
        return "display"
    elif campaign_type == "Performance Max":
        return "paidsearch"
    else:
        return None

# filter inputDf to skip rows where both utmmedium and utmcampaign are populated and copy rows to new filteredDf dataframe
filteredDf = inputDf[
    ~inputDf[RPT_COL_UTMMEDIUM].notna() | ~inputDf[RPT_COL_UTMCAMPAIGN].notna()
].copy()

# intialize outputDf so that it only includes rows that have been filtered
outputDf = filteredDf

# apply the functions to each row in the filtered dataframe and set result as the output dataframe value
outputDf[BULK_COL_UTMCAMPAIGN] = filteredDf[RPT_COL_CAMPAIGN].apply(set_utm_campaign)
outputDf[BULK_COL_UTMMEDIUM] = filteredDf[RPT_COL_CAMPAIGN_TYPE].apply(set_utm_medium)

print(tableize(outputDf.head()))

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