Purpose:

The Python script extracts and assigns a campaign type from campaign names based on a specific tag format.

To Elaborate

The script is designed to parse campaign names and extract a specific tag that identifies the campaign type, which is then assigned to a dimension called ‘Type de Campagne’. The tag is expected to appear after a hyphen (‘-‘) in the campaign name. This process involves iterating through a dataset of campaign information, identifying the tag using a regular expression, and updating the dataset with the extracted tag if it differs from the existing one. The script ensures that only campaigns with a valid tag are included in the final output, and it also removes any extra whitespace from campaign names to maintain data consistency.

Walking Through the Code

1. Configurable Parameters:
   • The script begins by defining a configurable parameter PLACEMENT_KEY, which is set to a hyphen (‘-‘). This key is used to identify the position of the tag within the campaign name.
   • It also specifies the primary data source and the relevant columns for processing, such as ‘Campaign’, ‘Account’, and ‘Type de Campagne’.
2. Tag Extraction Function:
   • A function get_tag_from_campaign_name is defined to extract the tag from the campaign name using a regular expression. The regex pattern is designed to capture the word following the third hyphen in the campaign name.
   • If a tag is found, it is returned after stripping any whitespace; otherwise, the entire campaign name is returned.
3. Data Processing:
   • The script creates a copy of the input data to work on, ensuring the original data remains unchanged.
   • It iterates over each row of the input data, checking if the campaign name contains the PLACEMENT_KEY. If not, the row is skipped.
   • For each valid campaign name, the script extracts the tag and compares it with the existing tag in the dataset. If the extracted tag is different and non-empty, it updates the dataset with the new tag; otherwise, it assigns a NaN value.
4. Final Output Preparation:
   • The script filters out rows with empty tags to ensure only valid entries are included in the output.
   • It removes any extra whitespace from the campaign names to prevent issues in data preview.
   • Finally, it prints the processed data if it is not empty, otherwise, it indicates that the output is empty.

Vitals

• Script ID : 1071
• Client ID / Customer ID: 1306927809 / 60270355
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Type de Campagne
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
• Created by Grégory Pantaine on 2024-05-10 16:50
• Last Updated by Grégory Pantaine on 2024-05-15 12:27

> See it in Action

Python Code

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## name: Script - Campaign - Type de Campagne
## description:
## Parse Campaign Name and pick out the campaign type into a dimension 'Type de campagne'.
## Tag appears after '-' in campaign name.
## 
## Copied by Grégory Pantaine
## created: 2024-05-10

########### 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_TYPE_CAMPAGNE = 'Type de Campagne'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_TYPE_CAMPAGNE = 'Type de Campagne'

# Function to extract tag from campaign name

def get_tag_from_campaign_name(campaign_name):
    regex_pattern = r"(?:[^-]+-){3}(\w+)"

    placement_match = re.search(regex_pattern, campaign_name)
    if placement_match:
        placement_value = placement_match.group(1)
        # print("Placement value:", placement_value)
        return placement_value.strip()
    else:
        print("Placement value not found: " + campaign_name)
    
    # Return the entire campaign name if 'pl:' is not present
    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_TYPE_CAMPAGNE]
    campaign_name = row[RPT_COL_CAMPAIGN]
    
    # Skip processing if campaign name does not contain the placement key
    if PLACEMENT_KEY 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_TYPE_CAMPAGNE] = tag
    else:
        outputDf.at[index, BULK_COL_TYPE_CAMPAGNE] = np.nan

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

# 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", tableize(outputDf.head()))
else:
    print("Empty outputDf")

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