Script 771: Script Campaign Bulk Sheet

Purpose

Python script to filter and manipulate data from a primary data source based on specific criteria.

To Elaborate

The Python script filters data from a primary data source based on certain criteria and performs operations on the filtered data. The script aims to exclude campaigns that have ended and only include campaigns with a specific type of traffic. It then applies necessary operations on the filtered data, such as updating the daily budget and pacing calculation date. The output is a modified version of the filtered data.

Walking Through the Code

  1. The script starts by importing the necessary libraries and defining the current date.
  2. It defines the columns of the primary data source and the output columns with their initial values.
  3. The first filter excludes campaigns that have the status ‘Campaign Ended’.
  4. The second filter further narrows down the data to only include campaigns with ‘SBA Traffic’ as ‘Traffic’.
  5. The necessary operations are applied on the filtered data, such as updating the daily budget and pacing calculation date.
  6. The modified data is stored in the output dataframe.
  7. Finally, the output dataframe is printed.

Note: The script assumes that the user may want to display or utilize the output dataframe.

Vitals

  • Script ID : 771
  • Client ID / Customer ID: 1306927187 / 60270139
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, Pacing Calculation Date
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
  • Created by ascott@marinsoftware.com on 2024-03-06 22:10
  • Last Updated by ascott@marinsoftware.com on 2024-03-06 22:11
> See it in Action

Python Code

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##
## name: Campaign Bulk Sheet
## description:
##  
## 
## author: 
## created: 2024-03-04
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_AUTO_PACING_CYCLE_START_DATE = 'Auto. Pacing Cycle Start Date'
RPT_COL_AUTO_PACING_CYCLE_END_DATE = 'Auto. Pacing Cycle End Date'
RPT_COL_AUTO_PACING_CYCLE_DAYS_ELAPSED = 'Auto. Pacing Cycle Days Elapsed'
RPT_COL_AUTO_PACING_CYCLE_DAYS_REMAINING = 'Auto. Pacing Cycle Days Remaining'
RPT_COL_AUTO_PACING_CYCLE_PACING = 'Auto. Pacing Cycle Pacing'
RPT_COL_AUTO_PACING_CYCLE_THRESHOLD = 'Auto. Pacing Cycle Threshold'
RPT_COL_TOTAL_TARGET_SPEND_PER_IMPRVIEWS = 'Total Target (Spend/Impr./Views)'
RPT_COL_TOTAL_DAYS = 'Total Days'
RPT_COL_TOTAL_DAYS_ELAPSED = 'Total Days Elapsed'
RPT_COL_TOTAL_PACING = 'Total Pacing'
RPT_COL_DELIVERY_STATUS = 'Delivery Status'
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PACING_CALCULATION_DATE = 'Pacing Calculation Date'
RPT_COL_SOCIAL_BUDGET = 'Social Budget'
RPT_COL_SOCIAL_BUDGET_UPDATE_STATUS = 'Social Budget Update Status'
RPT_COL_AUTO_PACING_CYCLE_PUB_COST = 'Auto. Pacing Cycle Pub. Cost'
RPT_COL_AUTO_PACING_CYCLE_IMPR = 'Auto. Pacing Cycle Impr.'
RPT_COL_AUTO_PACING_CYCLE_CLICKS = 'Auto. Pacing Cycle Clicks'
RPT_COL_AUTO_PACING_CYCLE_VIEWS = 'Auto. Pacing Cycle Views'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'

# 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>>"

# First filter: Exclude campaigns with 'Campaign Ended'
campaigns_not_ended = inputDf[inputDf[RPT_COL_AUTO_PACING_CYCLE_THRESHOLD] != 'Campaign Ended']

# Second filter: From the remaining, only include those where SBA Traffic is 'Traffic'
filteredDf = campaigns_not_ended[campaigns_not_ended[RPT_COL_SBA_TRAFFIC] == 'Traffic']

# Apply necessary operations on filteredDf
filteredDf.loc[:, RPT_COL_DAILY_BUDGET] = filteredDf[RPT_COL_RECOMMENDED_DAILY_BUDGET]
filteredDf.loc[:, RPT_COL_PACING_CALCULATION_DATE] = today
outputDf = filteredDf.copy()

# Assuming you want to display or utilize outputDf
print(outputDf)

Post generated on 2024-03-10 06:34:12 GMT

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