Script 767: Scripts 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 and performs operations on the filtered data to create an output dataframe. The script excludes campaigns that have ended and only includes campaigns with a specific SBA Traffic value. It then applies necessary operations on the filtered data, such as updating the daily budget and pacing calculation date. The final output is a dataframe that can be displayed or utilized for further analysis.

Walking Through the Code

  1. The script starts by getting the current date.
  2. It defines the primary data source and column constants.
  3. It creates an output dataframe with the column “Daily Budget” and an initial value.
  4. It filters the primary data source to exclude campaigns that have ended.
  5. It further filters the remaining campaigns to only include those with a specific SBA Traffic value.
  6. It applies necessary operations on the filtered dataframe, such as updating the daily budget and pacing calculation date.
  7. The final output dataframe is a copy of the filtered dataframe.
  8. The output dataframe is printed for display or further utilization.

Vitals

  • Script ID : 767
  • Client ID / Customer ID: 1306927185 / 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 22:00
  • Last Updated by ascott@marinsoftware.com on 2024-03-06 22:02
> 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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