Purpose

The Python script extracts a title from the “Group” column using a regex pattern and inserts it into the “Title” column.

To Elaborate

The Python script is designed to process a DataFrame by extracting a specific part of the text from the “Group” column and placing it into a new “Title” column. This extraction is performed using a regular expression pattern that identifies the desired portion of the text, which is typically the title before any underscore or the phrase “ - Season”. The script also standardizes the capitalization of possessive forms by converting any occurrence of “‘S” to “‘s” in the extracted titles. Additionally, it marks each processed row with a “YES” in the ‘g-check’ column to indicate successful processing. This script is useful for data cleaning and preparation, particularly in scenarios where structured data needs to be extracted from a more complex text field.

Walking Through the Code

1. Data Preparation
   • The script begins by defining the primary data source and relevant column names, such as ‘Group’, ‘Campaign’, ‘Account’, and ‘Title’.
   • It then copies the input DataFrame to an output DataFrame to preserve the original data.
2. Title Extraction
   • A regular expression pattern r'^(.*?)(?:[_]|(?= - Season))' is defined to extract the title from the “Group” column.
   • The script applies this regex pattern to each entry in the “Group” column, extracting the title and assigning it to the “Title” column.
3. Text Standardization
   • The script replaces any occurrence of “‘S” with “‘s” in the “Title” column to ensure consistent capitalization of possessive forms.
4. Processing Indicator
   • It sets the ‘g-check’ column to “YES” for all rows, indicating that the processing has been completed.
5. Output
   • The processed DataFrame is returned, containing the newly extracted titles and updated ‘g-check’ status.

Vitals

• Script ID : 1293
• Client ID / Customer ID: 1306912147 / 69058
• Action Type: Bulk Upload
• Item Changed: AdGroup
• Output Columns: Account, Campaign, Group, Title
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jeremy Brown (jbrown@marinsoftware.com)
• Created by Jeremy Brown on 2024-07-26 10:55
• Last Updated by Jeremy Brown on 2024-07-31 11:49

> See it in Action

Python Code

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##
## Name: Extract Title from Group name
## description: Use the regex pattern = r'^(.*?)(?:[_]|(?= - Season))' to extract the title from the "Group" column and insert into `Title` dimension column.
## 
## author: Jeremy Brown 
## created: 2024-07-26
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_GROUP = 'Group'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_TITLE = 'Title'
RPT_COL_IMPR = 'Impr.'
RPT_COL_STATUS = 'Campaign Status'
RPT_COL_STATUS = 'Group Status'
RPT_COL_CHECK = 'g-check'

# Function to process the input DataFrame and return the output DataFrame
def process(inputDf):
    # Define the regex pattern to extract the title from the "Group" column
    regex_pattern = r'^(.*?)(?:[_]|(?= - Season))'
    
    # Copy the input DataFrame to the output DataFrame
    outputDf = inputDf.copy()

    # Extract the title using the regex pattern and assign it to the "Title" column
    outputDf[RPT_COL_TITLE] = outputDf[RPT_COL_GROUP].apply(
        lambda x: re.match(regex_pattern, x).group(1) if re.match(regex_pattern, x) else ""
    )
    
    # Convert any occurrence of "'S" to "'s" in the 'Title' column
    outputDf[RPT_COL_TITLE] = outputDf[RPT_COL_TITLE].str.replace("'S", "'s")

    # Set the 'g-check' column to "YES"
    outputDf[RPT_COL_CHECK] = "YES"
    
    # Print the changed data for debugging
    print(outputDf)

    return outputDf

# Trigger the main process
outputDf = process(inputDf)

Post generated on 2024-11-27 06:58:46 GMT