Purpose:

The Python script extracts and tags the campaign strategy from the campaign name based on specific delimiters.

To Elaborate

The Python script is designed to process a dataset containing campaign information, specifically focusing on extracting a segment of the campaign name that represents the campaign strategy. The campaign names are structured with delimiters, specifically dashes (‘-‘), and the script identifies the segment located between the first and second dash. This extracted segment is then tagged as the campaign strategy. The script ensures that only campaign names with at least two dashes are processed, and it updates the dataset with the extracted strategy, removing any rows where the strategy could not be determined. This process helps in organizing and categorizing campaigns based on their strategic focus, facilitating better analysis and reporting.

Walking Through the Code

1. Configurable Parameters:
   • The script begins by defining a configurable parameter PLACEMENT_KEY, which is set to a dash (‘-‘). This key is used to split the campaign names and extract the strategy.
2. Data Source Initialization:
   • The script initializes the primary data source inputDf from a dictionary dataSourceDict, which contains campaign data.
3. Function Definition:
   • A function get_value_between_dashes is defined to extract the segment between the first and second dash in a campaign name. It splits the name using the PLACEMENT_KEY and checks if there are more than two parts.
4. Data Processing:
   • The script copies all rows from inputDf to outputDf for processing.
   • It iterates over each row in inputDf, checking if the campaign name contains at least two dashes. If not, it skips processing for that row.
5. Tagging Strategy:
   • For valid campaign names, the script uses the function to extract the strategy and updates the outputDf with this value in the BULK_COL_CAMPAIGN_STRATEGY column, ensuring only non-empty tags are retained.
6. Data Cleanup:
   • The script removes rows with empty strategy tags from outputDf and strips extra whitespace from campaign names to ensure clean data presentation.
7. Output Handling:
   • Finally, the script checks if outputDf is empty and prints the first few rows if it contains data, otherwise it prints a message indicating an empty dataset.

Vitals

• Script ID : 1525
• Client ID / Customer ID: 1306928453 / 60270539
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Campaign Strategy, Campaign ID
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
• Created by Grégory Pantaine on 2024-11-14 17:24
• Last Updated by emerryfield@marinsoftware.com on 2025-03-05 19:20

> See it in Action

Python Code

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##
## name: Script AutoTag - Campaign Strategy
## description: Tags the value after the first - and before the second - in the campaign name,
## ie: Credit Cards 
## 
## author: G Pantaine with help from ChatGPT & M Huang.
## created: 2024-11-14
## 
# Configurable Params - START
PLACEMENT_KEY = '-'

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

# Output columns and initial values
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_STRATEGY = 'Campaign Strategy'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CAMPAIGN_STRATEGY = 'Campaign Strategy'
BULK_COL_CAMPAIGN_ID = 'Campaign ID'

# Function to extract the value after the first '-' and before the second '-'
def get_value_between_dashes(campaign_name):
    parts = campaign_name.split(PLACEMENT_KEY)
    if len(parts) > 2:
        return parts[1].strip()
    else:
        return np.nan

# Copy all input rows to output
outputDf = inputDf.copy()

# Loop through all rows
for index, row in inputDf.iterrows():
    campaign_name = row[RPT_COL_CAMPAIGN]
    
    # Skip processing if campaign name does not contain at least two placement keys
    if campaign_name.count(PLACEMENT_KEY) < 2:
        continue

    value = get_value_between_dashes(campaign_name)

    # Only tag if it's different than the existing tag
    if pd.notna(value):
        outputDf.at[index, BULK_COL_CAMPAIGN_STRATEGY] = value
    else:
        outputDf.at[index, BULK_COL_CAMPAIGN_STRATEGY] = np.nan

# Only include non-empty tags in bulk
outputDf = outputDf.dropna(subset=[BULK_COL_CAMPAIGN_STRATEGY])

# Remove extra whitespace from campaign name that breaks Preview
outputDf[RPT_COL_CAMPAIGN] = outputDf[RPT_COL_CAMPAIGN].str.strip()

if not outputDf.empty:
    print("outputDf", outputDf.head().to_string())
else:
    print("Empty outputDf")

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