Script 1725: Dimension Tags from Campaign Name

Purpose:

The script extracts pacing dates and goals from campaign names in a dataset to populate missing information.

To Elaborate

The Python script is designed to process a dataset containing campaign information, specifically focusing on extracting pacing start and end dates, as well as the campaign goal, from the campaign names. This is particularly useful when such information is not explicitly provided in separate columns. The script uses regular expressions to identify date patterns and goal keywords within the campaign names. It then converts these extracted strings into date objects and assigns appropriate goal values based on predefined rules. The processed data is filtered to include only rows where new information has been successfully extracted, ensuring that the output dataset is enriched with the necessary pacing and goal details.

Walking Through the Code

  1. Date Conversion Function:
    • A function convert_date is defined to handle date strings, attempting to parse them first as four-digit years and then as two-digit years if necessary.
  2. Information Extraction Function:
    • The extract_info_from_campaign_name_enhanced function uses regular expressions to extract date ranges and goal types from campaign names.
    • It identifies patterns for dates and goals, handling various delimiters and formats.
    • The function determines the goal based on specific keywords found in the campaign name.
  3. Data Preparation:
    • The script renames columns in the input DataFrame to match expected names and removes any unnecessary columns.
    • It filters the DataFrame to focus on rows where the goal information is missing, ensuring campaign names are treated as strings.
  4. Data Processing:
    • New columns for pacing start date, end date, and goal are added to the filtered DataFrame.
    • The script iterates over each row, applying the extraction function to populate these new columns with extracted data.
  5. Data Filtering and Output:
    • The DataFrame is further filtered to retain only rows where valid information has been extracted.
    • A new DataFrame is created with selected columns, which is then printed to display the enriched campaign data.

Vitals

  • Script ID : 1725
  • Client ID / Customer ID: 1306927663 / 60270139
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Goal, Pacing - End Date, Pacing - Start Date
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Arayla Caldwell (acaldwell@marinsoftware.com)
  • Created by Arayla Caldwell on 2025-02-07 22:05
  • Last Updated by Mingxia Wu on 2025-02-28 06:08
> See it in Action

Python Code

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## name: Dimension Tags from Campaign Name
## description: Extracts pacing dates and goal from campaign name
## author: 
## created: 2023-12-04
## 7/1 Updated version with CPM and MS for Social Campaigns 
## 6/27 Updated version w/o 'Target (Impr/Spend/Views)'

# Column Definitions
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PACING_START_DATE = 'Pacing - Start Date'
RPT_COL_PACING_END_DATE = 'Pacing - End Date'
RPT_COL_GOAL = 'Goal'

# Function to convert date with enhanced logic to handle two-digit years
def convert_date(date_str):
    if date_str is None:
        return None
    try:
        # Try to parse date with four-digit year
        return datetime.datetime.strptime(date_str, '%m/%d/%Y').date()
    except ValueError:
        try:
            # Try to parse date with two-digit year
            return datetime.datetime.strptime(date_str, '%m/%d/%y').date()
        except ValueError:
            return None

# Function to extract information from the campaign name with enhanced logic
def extract_info_from_campaign_name_enhanced(campaign_name):
    # Updated date pattern to handle various delimiters and formats
    date_pattern = r'\(?(\d{1,2}/\d{1,2}/\d{2,4})[-\s_]+(\d{1,2}/\d{1,2}/\d{2,4})\)?'
    goal_pattern = r'(Search|RGD|SGD|SBD|CPM|MS)'
    
    date_match = re.search(date_pattern, campaign_name)
    goal_match = re.search(goal_pattern, campaign_name)
    
    start_date_str, end_date_str = (date_match.groups() if date_match else (None, None))
    goal_segment = goal_match.group(1) if goal_match else None
    
    if 'CPM' in campaign_name:
        goal = 'CPM'
    elif 'MS' in campaign_name:
        goal = 'MS'
    else:
        goal = 'MS' if goal_segment and 'Search' in goal_segment else 'CPM' if goal_segment in ['RGD', 'SGD', 'SBD'] else ''
    
    start_date = convert_date(start_date_str)
    end_date = convert_date(end_date_str)
    
    return start_date, end_date, goal

# Rename columns to match the script's expectations
inputDf.columns = ['Campaign', 'Account', 'Pacing - Start Date', 'Pacing - End Date', 'Goal', 'Unnamed']

# Drop the 'Unnamed' column
inputDf.drop(columns=['Unnamed'], inplace=True)

# Clean any leading/trailing spaces in the column names
inputDf.columns = inputDf.columns.str.strip()

# Filter for rows where the Goal column is blank and create a copy to avoid SettingWithCopyWarning
inputDf_filtered = inputDf[inputDf[RPT_COL_GOAL].isna()].copy()

# Ensure that the campaign names are treated as strings to avoid TypeError
inputDf_filtered[RPT_COL_CAMPAIGN] = inputDf_filtered[RPT_COL_CAMPAIGN].astype(str)

# Adding columns for extracted information to inputDf_filtered
inputDf_filtered[RPT_COL_PACING_START_DATE] = np.nan
inputDf_filtered[RPT_COL_PACING_END_DATE] = np.nan
inputDf_filtered[RPT_COL_GOAL] = np.nan

# Process each row in inputDf_filtered to extract information
for index, row in inputDf_filtered.iterrows():
    start_date, end_date, goal = extract_info_from_campaign_name_enhanced(row[RPT_COL_CAMPAIGN])
    if start_date or end_date or goal:  # Include rows with any valid extracted information
        inputDf_filtered.loc[index, RPT_COL_PACING_START_DATE] = start_date
        inputDf_filtered.loc[index, RPT_COL_PACING_END_DATE] = end_date
        inputDf_filtered.loc[index, RPT_COL_GOAL] = goal

# Filter inputDf_filtered for rows with extracted information
filteredDf = inputDf_filtered.dropna(subset=[RPT_COL_GOAL])

# Define the columns to be included in the output DataFrame
cols = [
    RPT_COL_CAMPAIGN,
    RPT_COL_ACCOUNT,
    RPT_COL_PACING_START_DATE,
    RPT_COL_PACING_END_DATE,
    RPT_COL_GOAL
]

# Create output DataFrame with the selected columns
outputDf = filteredDf[cols].copy()

# Print the output DataFrame to check the extracted information
print("Output DataFrame with extracted information:")
print(outputDf)

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

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