Purpose

The Python script calculates and stores the average cost-per-click (CPC) for the last 28 days in a dimension called “Test.”

To Elaborate

The Python script is designed to process advertising data by extracting and storing the average cost-per-click (CPC) for the last 28 days into a specific dimension named “Test.” This is achieved by reading data from a primary data source, checking for the presence of the “Avg. CPC $” column, and then transferring this data into a structured output format. The script ensures that the relevant columns, such as “Account” and “Campaign,” are preserved and transferred to the output data frame. If the “Avg. CPC $” column is not found, the script handles this by setting the “Test” column to None, ensuring robustness in data processing.

Walking Through the Code

1. Data Initialization
   • The script begins by defining the primary data source, inputDf, which contains the advertising data.
   • It initializes an empty DataFrame, outputDf, with columns for “Account,” “Campaign,” and “Test.”
2. Data Transfer and Validation
   • The script copies the “Account” and “Campaign” columns from inputDf to outputDf.
   • It checks if the “Avg. CPC $” column exists in inputDf. If it does, the values are copied to the “Test” column in outputDf. If not, the “Test” column is set to None, and a message is printed to indicate the missing column.
3. Output Display
   • Finally, the script prints the outputDf to display the structured data with the average CPC values.

Vitals

• Script ID : 733
• Client ID / Customer ID: 6968245 / 3374365
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Test
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: wmaclaggan@marinsoftware.com (wmaclaggan@marinsoftware.com)
• Created by wmaclaggan@marinsoftware.com on 2024-03-01 23:11
• Last Updated by wmaclaggan@marinsoftware.com on 2024-03-01 23:24

> See it in Action

Python Code

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##
## name: WJM Test
## description:
##  
## 
## author: 
## created: 2024-03-01
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
print(tableize(inputDf))

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_TEST = 'Test'

# Initialize outputDf with the required columns
outputDf = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_TEST])

# Assuming 'Account' and 'Campaign' are columns in inputDf, copy them to outputDf
outputDf[BULK_COL_ACCOUNT] = inputDf['Account']
outputDf[BULK_COL_CAMPAIGN] = inputDf['Campaign']

# Check if 'Avg. CPC' column exists in inputDf and copy it to 'Test' in outputDf
if 'Avg. CPC $' in inputDf.columns:
    outputDf[BULK_COL_TEST] = inputDf['Avg. CPC $']
else:
    print("'Avg. CPC' column not found in inputDf")
    outputDf[BULK_COL_TEST] = None

# user code start here
print(tableize(outputDf))

Post generated on 2024-11-27 06:58:46 GMT