Purpose:

The Python script extracts project numbers from campaign names by identifying tags that appear after a specified delimiter and assigns them to a designated dimension.

To Elaborate

The script is designed to parse campaign names and extract project numbers, which are identified as tags following a specific delimiter, in this case, a hyphen (‘-‘). The extracted project numbers are then assigned to a dimension called ‘Numero de Projet’. This process involves iterating through a dataset of campaign names, checking for the presence of the delimiter, and using a regular expression to capture the tag that follows it. If a tag is found and differs from any existing tag, it is updated in the output dataset. The script ensures that only non-empty tags are included in the final output, and it removes any extra whitespace from campaign names to prevent issues in data preview.

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 in the campaign name where the project number tag appears.
   • It also specifies the primary data source and column names for input and output data frames.
2. Function Definition:
   • A function get_tag_from_campaign_name is defined to extract the tag from the campaign name using a regular expression pattern. This pattern searches for a sequence of word characters following the hyphen.
3. Data Processing:
   • The script copies all rows from the input data frame to the output data frame.
   • It iterates through each row of the input data frame, checking if the campaign name contains the PLACEMENT_KEY.
   • If the key is present, the script extracts the tag using the defined function and compares it with the existing tag in the ‘Numero de Projet’ column.
   • If the extracted tag is different and non-empty, it updates the output data frame with the new tag; otherwise, it assigns a NaN value.
4. Final Adjustments:
   • The script filters the output data frame to include only rows with non-empty tags.
   • It removes extra whitespace from campaign names to ensure clean data presentation.
   • Finally, it prints the output data frame if it is not empty, providing a preview of the processed data.

Vitals

• Script ID : 1067
• Client ID / Customer ID: 1306927809 / 60270355
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Numero de Projet
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
• Created by Grégory Pantaine on 2024-05-10 16:33
• Last Updated by Grégory Pantaine on 2024-05-15 12:25

> See it in Action

Python Code

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## name: Script - Campaign - Numero de Projet
## description:
## Parse Campaign Name and pick out project number into a dimension 'Numero de Projet'.
## 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_NUMERO_PROJET = 'Numero de Projet'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_NUMERO_PROJET = 'Numero de Projet'

# Function to extract tag from campaign name

def get_tag_from_campaign_name(campaign_name):
    regex_pattern = r"-(\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_NUMERO_PROJET]
    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_NUMERO_PROJET] = tag
    else:
        outputDf.at[index, BULK_COL_NUMERO_PROJET] = np.nan

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

# 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