Purpose:

The Python script parses campaign names to automatically tag them with a school dimension based on the text before the first underscore.

To Elaborate

The script is designed to automate the process of tagging campaign names with a specific dimension related to schools. It extracts a tag from the campaign name, which is the text appearing before the first underscore (‘_’). This tag is then used to update the ‘School’ column in the dataset. The script ensures that only campaigns with a separator in their name are processed, and it updates the tag only if it differs from the existing one. This helps maintain accurate and consistent tagging across campaigns, facilitating better organization and analysis of campaign data.

Walking Through the Code

1. Configurable Parameters:
   • The script begins by defining configurable parameters such as the separator (SEP) and the location of the tag (TAG_LOCATION). These parameters can be adjusted by the user to change how the campaign names are parsed.
   • The primary data source (inputDf) is specified, along with the columns used for reporting and bulk operations.
2. Tag Extraction Function:
   • A function get_tag_from_campaign_name is defined to extract the tag from the campaign name. It checks for the presence of the separator and splits the name accordingly to retrieve the tag.
3. Data Processing:
   • The script copies all input rows to an output DataFrame (outputDf) and iterates through each row.
   • For each campaign name, it checks if the separator is present. If not, the row is skipped.
   • The extracted tag is compared with the existing tag in the ‘School’ column. If they differ, the new tag is updated; otherwise, a NaN value is assigned.
4. Output Filtering:
   • The script filters out rows with empty tags, ensuring only non-empty tags are included in the final output.
   • It prints the output DataFrame if it is not empty, providing a tabular view of the processed data.

Vitals

• Script ID : 687
• Client ID / Customer ID: 1306926629 / 60270083
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, School, Campaign ID
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
• Created by dwaidhas@marinsoftware.com on 2024-02-08 21:55
• Last Updated by dwaidhas@marinsoftware.com on 2024-11-21 16:19

> See it in Action

Python Code

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##
## name: Campaign Name Dimension Auto Tagging: School
## description:
## Parse Campaign Name and add Campaign-level Marin Dimensions Tag for Program.
## Tag appears before the first '_' underscore in campaign name.
## 
## author: Dana Waidhas 
## created: 2024-02-08
## 

########### Configurable Params - START ##########
SEP = '_'
TAG_LOCATION = 1  # Comes after the first separator
# Primary data source and columns
inputDf = dataSourceDict["1"]

# Output columns and initial values
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
RPT_COL_SCHOOL = 'School'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CAMPAIGN_ID = 'Campaign ID'
BULK_COL_SCHOOL = 'School'


def get_tag_from_campaign_name(campaign_name):
    # Check if the separator is present in the campaign name
    if SEP in campaign_name:
        # Split only after the first underscore
        vals = campaign_name.split(SEP, 1)
        tag = vals[0].strip() if len(vals) > 0 else campaign_name.strip()
        return tag
    else:
        # Return the entire campaign name if no separator is found
        return campaign_name.strip()

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

# Loop through all rows
for index, row in inputDf.iterrows():
    existing_tag = row[RPT_COL_SCHOOL]
    campaign_name = row[RPT_COL_CAMPAIGN]
    
    # Skip processing if campaign name does not contain the separator
    if SEP not in campaign_name:
        continue

    tag = get_tag_from_campaign_name(campaign_name)

    # Print campaign and tag information
    print("Campaign [%s] => Tag [%s]" % (campaign_name, tag))

    # Only tag if it's different than the existing tag
    if (len(tag) > 0) & (tag != existing_tag):
        outputDf.at[index, BULK_COL_SCHOOL] = tag
    else:
        outputDf.at[index, BULK_COL_SCHOOL] = np.nan


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


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

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