Script 587: ABO Budget Assignment

Purpose:

The Python script assigns corrected budget values at the campaign level to a dimension called “Meta ABO Budget” and labels campaigns as using ABO Budgets by setting the “Budget Level” dimension to ABO.

To Elaborate

The script processes advertising data to allocate and label budgets at the campaign level. It takes input data related to ad sets and their budget types, then assigns a corrected budget value to a dimension called “Meta ABO Budget.” Additionally, it labels campaigns as using ABO Budgets by setting the “Budget Level” dimension to ABO. The script includes logic to map ad accounts to specific names and filters out any accounts that are not recognized. This ensures that only valid and correctly labeled data is processed and outputted, which is crucial for accurate budget allocation and reporting.

Walking Through the Code

  1. Account Mapping Function:
    • The get_account function maps ad account names to specific account identifiers. It checks for specific keywords in the ad account name and returns a corresponding mapped name. If no match is found, it returns ‘Unknown’.
  2. Data Preparation:
    • The script reads input data from a primary data source into a DataFrame named inputDf.
    • A new column ‘Account’ is created in inputDf by applying the get_account function to the ‘Ad Account’ column.
  3. Data Aggregation:
    • The script groups the data by ‘Publisher Campaign ID’ and aggregates values such as ‘Campaign Name’, ‘Account’, and the sum of ‘Ad Set Daily Budget $’.
    • The aggregated data is stored in a new DataFrame called outputDf.
  4. Column Renaming and Budget Level Assignment:
    • The columns in outputDf are renamed to match the required output format, including renaming the aggregated budget to ‘Meta ABO Budget’.
    • A new column ‘Budget Level’ is added to outputDf, with all values set to ‘ABO’.
  5. Filtering:
    • The script filters out any rows in outputDf where the ‘Account’ is labeled as ‘Unknown’, ensuring only recognized accounts are included in the final output.

Vitals

  • Script ID : 587
  • Client ID / Customer ID: 1306926715 / 60270103
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Budget Level, Meta ABO Budget
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Chris Jetton (cjetton@marinsoftware.com)
  • Created by Chris Jetton on 2023-12-14 19:20
  • Last Updated by Chris Jetton on 2024-01-12 00:40
> See it in Action

Python Code

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##
## name: ABO Budget Assignment
## description: assigns values to Meta ABO Budget and Budget Level dimensions if applicable
##  
## 
## author: Chris Jetton
## created: 2023-12-07
## 

def get_account(ad_account):
    if 'South Carolina 22' in ad_account:
        return 'South Carolina 22 - Meta'
    elif 'Engage WI' in ad_account:
        return 'Engage WI - Meta'
    elif 'Indiana22' in ad_account:
        return 'Indiana 22 - Meta'
    elif 'Engage NH' in ad_account:
        return 'NH - Meta'
    elif 'Graduation Alliance' in ad_account:
        return 'Graduation Alliance - Meta'
    elif 'Kansas 20' in ad_account:
        return 'KANSAS 20 - Meta'
    elif 'Arizona21' in ad_account:
        return 'Arizona 21 - Meta'
    elif 'Michigan23' in ad_account:
        return 'Michigan23+ - Meta'
    elif 'Arkansas 21' in ad_account:
        return 'Arkansas 21+ - Meta'
    elif 'AchievePoint' in ad_account:
        return 'AchievePoint Career Academy - Meta'
    elif 'American Academy' in ad_account:
        return 'The American Academy (new) - Meta'
    elif 'Engage NM' in ad_account:
        return 'Engage NM - Meta'
    elif 'Engage SC' in ad_account:
        return 'Engage SC - Meta'
    elif 'Engage MI' in ad_account:
        return 'Engage MI - Meta'
    elif 'Engage AZ' in ad_account:
        return 'Engage AZ - Meta'
    elif 'Engage GA' in ad_account:
        return 'Engage GA - Meta'
    elif 'Engage OH' in ad_account:
        return 'Engage OH - Meta'
    elif 'Missouri 21' in ad_account:
        return 'Missouri 21+ - Meta'
    else:
        return 'Unknown'

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

# primary data source and columns
inputDf = dataSourceDict["1"]

# Output columns and initial values
RPT_COL_AD_SET_NAME = 'Ad Set Name'
RPT_COL_AD_ACCOUNT = 'Ad Account'
RPT_COL_CAMPAIGN_NAME = 'Campaign Name'
RPT_COL_PUBLISHER_CAMPAIGN_ID = 'Publisher Campaign ID'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_CAMPAIGN_BUDGET_TYPE = 'Campaign Budget Type'
RPT_COL_CAMPAIGN_DAILY_BUDGET = 'Campaign Daily Budget $'
RPT_COL_PUBLISHER_AD_SET_ID = 'Publisher Ad Set ID'
RPT_COL_AD_SET_STATUS = 'Ad Set Status'
RPT_COL_AD_SET_BUDGET_TYPE = 'Ad Set Budget Type'
RPT_COL_AD_SET_DAILY_BUDGET = 'Ad Set Daily Budget $'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_META_ABO_BUDGET = 'Meta ABO Budget'
BULK_COL_BUDGET_LEVEL_ABO = 'Budget Level'

# Create a new column 'Account' using the get_account function
inputDf['Account'] = inputDf['Ad Account'].apply(get_account)

# Group by Campaign ID, aggregate the values, and create the outputDf
outputDf = inputDf.groupby('Publisher Campaign ID').agg({
    'Campaign Name': 'first',
    'Account': 'first',
    'Ad Set Daily Budget $': 'sum'
}).reset_index()

# Rename columns as per your requirements
outputDf.rename(columns={
    'Campaign Name': 'Campaign',
  # 'Publisher Campaign ID': 'Campaign',
    'Account': 'Account',
    'Ad Set Daily Budget $': 'Meta ABO Budget'
}, inplace=True)

# Add Budget Level column
outputDf['Budget Level'] = 'ABO'

# Filter out rows with Account = 'Unknown'
outputDf = outputDf[outputDf['Account'] != 'Unknown']

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

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