Script 1519: Dynamic Allocation Temp Traffic

Purpose:

The Python script dynamically allocates temporary traffic budgets based on recommended daily budgets from input data.

To Elaborate

The Python script is designed to manage and allocate temporary traffic budgets for advertising campaigns. It processes input data containing various campaign metrics and recommendations, specifically focusing on the recommended daily budget for each campaign. The script extracts relevant information from the input data and constructs an output DataFrame that includes the account, campaign, and the recommended daily budget. This output can be used to adjust and optimize budget allocations for advertising campaigns, ensuring that resources are allocated efficiently based on the provided recommendations.

Walking Through the Code

  1. Data Preparation
    • The script begins by defining the primary data source, inputDf, which contains campaign-related data.
    • It identifies and sets up the necessary columns from the input data, such as ‘Account’, ‘Campaign’, and ‘Recommended Daily Budget’.
  2. Output DataFrame Creation
    • An empty DataFrame, outputDf, is created to store the processed data.
    • The script populates outputDf with the ‘Account’ and ‘Campaign’ columns from inputDf.
    • It assigns the ‘Recommended Daily Budget’ from inputDf to the ‘Daily Budget’ column in outputDf.
  3. Output Display
    • The script prints the first few rows of outputDf to provide a preview of the processed data.
    • It also prints a tableized version of the first few rows of inputDf for reference.

Vitals

  • Script ID : 1519
  • Client ID / Customer ID: 1306922277 / 60268979
  • Action Type: Bulk Upload (Preview)
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
  • Created by ascott@marinsoftware.com on 2024-11-13 14:28
  • Last Updated by ascott@marinsoftware.com on 2024-12-04 16:08
> See it in Action

Python Code

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##
## name: Dynamic Allocation Temp Traffic
## description:
##  
## 
## author: 
## created: 2024-11-13
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_STRATEGY_TARGET = 'Strategy Target'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CTR = 'CTR %'
RPT_COL_CONV = 'Conv.'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_IMPR_SHARE = 'Impr. share %'
RPT_COL_LOST_IMPR_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_LOST_IMPR_SHARE_RANK = 'Lost Impr. Share (Rank) %'
RPT_COL_SHARED_BUDGET = 'Shared Budget'
RPT_COL_PUBLISHER_BID_PORTFOLIO_NAME = 'Publisher Bid Portfolio Name'
RPT_COL_PUBLISHER_BID_STRATEGY = 'Publisher Bid Strategy'
RPT_COL_PUBLISHER_TARGET_CPA = 'Publisher Target CPA'
RPT_COL_PUBLISHER_TARGET_ROAS = 'Publisher Target ROAS'
RPT_COL_UNCONSTRAINED_RECOMMENDED_CPA_PER_ROAS_TARGET = 'Unconstrained Recommended CPA/ROAS Target'
RPT_COL_RECOMMENDED_CPA_PER_ROAS_TARGET = 'Recommended CPA/ROAS Target'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_UNCONSTRAINED_RECOMMENDED_DAILY_BUDGET = 'Unconstrained Recommended Daily Budget'
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'
RPT_COL_RECOMMENDATION_CONSTRAINTS = 'Recommendation Constraints'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
RPT_COL_DAILY_BUDGET_RECOMMENDATION_DATE = 'Daily Budget Recommendation Date'
RPT_COL_CPA_PER_ROAS_TARGET_RECOMMENDATION_DATE = 'CPA/ROAS Target Recommendation Date'
RPT_COL_PUBLISHER_BID_STRATEGY_SYSTEM_STATUS = 'Publisher Bid Strategy System Status'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
outputDf[BULK_COL_DAILY_BUDGET] = "<<YOUR VALUE>>"


# Assuming CLIENT_TIMEZONE is defined somewhere in your code
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'

# Create the output DataFrame
outputDf = pd.DataFrame()
outputDf[BULK_COL_ACCOUNT] = inputDf['Account']
outputDf[BULK_COL_CAMPAIGN] = inputDf['Campaign']
outputDf[BULK_COL_DAILY_BUDGET] = inputDf[RPT_COL_RECOMMENDED_DAILY_BUDGET]

# Print the first few rows of the output DataFrame
print(outputDf.head())

print(tableize(inputDf.head()))

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

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