Purpose:

The Python script calculates the remaining days for each campaign based on its start date and updates a CSV file with this information.

To Elaborate

The Python script is designed to manage and pace the daily budget for various marketing campaigns by calculating the number of days remaining for each campaign. It extracts the campaign’s start date from its name, computes the remaining days until the campaign’s end date, and updates this information in a CSV file. This process helps in structured budget allocation (SBA) by ensuring that the budget is paced appropriately over the campaign’s duration. The script is particularly useful for marketing teams who need to monitor and adjust their campaign budgets dynamically based on the time left for each campaign.

Walking Through the Code

1. Data Source Initialization: The script begins by defining the primary data source, which is a dictionary containing campaign data. It initializes the DataFrame inputDf from this data source.

2. Output DataFrame Setup: It sets up an output DataFrame with columns for account, campaign, days elapsed, and days remaining. Initial values for days elapsed and remaining are placeholders.

3. Timezone Configuration: The script sets the client timezone to ‘America/Denver’ for consistent date and time calculations.

4. Sample DataFrame Creation: A sample DataFrame df is created with campaign data. This is a placeholder for actual data loading logic.

5. Function Definition: The calculate_remaining_days function is defined to compute the remaining days for a campaign. It splits the campaign name to extract the start date, converts it to a timezone-aware datetime object, and calculates the difference in days from the current date.

6. Apply Function to DataFrame: The function is applied to the ‘Campaign’ column of the DataFrame to create a new column ‘DAYS_REMAINING’, which stores the calculated remaining days for each campaign.

7. Output to CSV: Finally, the updated DataFrame is saved to a CSV file, allowing for further analysis or reporting. The file path is user-changeable, enabling flexibility in where the output is stored.

Vitals

• Script ID : 549
• Client ID / Customer ID: 1306926843 / 60270139
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Days Elapsed, Days Remaining
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jesus Garza (jgarza@marinsoftware.com)
• Created by Jesus Garza on 2023-11-29 15:34
• Last Updated by Jesus Garza on 2023-12-06 04:01

> See it in Action

Python Code

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##
## name: Budget Pacing
## description:
##  Pace Daily Budget for each Campaign 
## 
## author: Jesus Garza
## created: 2023-11-29
## 

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

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAYS_ELAPSED = 'Days Elapsed'
BULK_COL_DAYS_REMAINING = 'Days Remaining'
outputDf[BULK_COL_DAYS_ELAPSED] = "<<YOUR VALUE>>"
outputDf[BULK_COL_DAYS_REMAINING] = "<<YOUR VALUE>>"

# Set timezone
CLIENT_TIMEZONE = 'America/Denver'

today = datetime.datetime.now().astimezone(pytz.timezone(CLIENT_TIMEZONE)).date()

# Sample DataFrame creation (replace this with your data loading logic)
data = {'Campaign': ['Client1_10/2/2023-12/31/2023_CPM_1300', 'Client2_11/15/2023-12/31/2023_CPC_1500']}
df = pd.DataFrame(data)

def calculate_remaining_days(campaign_name):
    # Split the campaign name using '_' as a separator
    parts = campaign_name.split('_')

    if len(parts) == 3:
        # Extract the flight date from the second part of the name
        flight_date_str = parts[1]

        # Convert the flight date string to a datetime object
        flight_date_naive = datetime.datetime.strptime(flight_date_str, "%m/%d/%Y")
        
        # Convert the naive datetime to aware datetime
        flight_date = flight_date_naive.astimezone(pytz.timezone(CLIENT_TIMEZONE))

        # Calculate the remaining days
        remaining_days = (flight_date - datetime.datetime.now().astimezone(pytz.timezone(CLIENT_TIMEZONE))).days

        return remaining_days
    else:
        return None

# Apply the function to the 'Campaign' column and create a new column 'DAYS_REMAINING'
df['DAYS_REMAINING'] = df['Campaign'].apply(calculate_remaining_days)

# Save the DataFrame back to the CSV file with the new column
df.to_csv('/Users/jgarza/Desktop/days_remaining.csv', index=False)  # Replace 'output_file.csv' with the desired output file path

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