How This Workflow Works
This workflow takes a dataset, extracts its column names, applies cleaning rules such as capitalizing and removing dashes, and then automatically updates the dataset with these improved column names. The result is a dataset with clear, standardized column headers, making it easier to use in downstream tasks.
Key Features:
- Standardizes column names for consistency
- Removes unwanted characters to improve readability
- Automates the renaming process to save manual effort
Step-by-step:
1. Extract and Clean Column Names:
The workflow first extracts all column names in the dataset and applies cleaning rules, such as capitalizing each name and removing dashes. This ensures that column headers follow a consistent and readable format.
2. Apply Improved Column Names to Data:
After cleaning, the workflow automatically updates the original dataset with the new, standardized column names. This step ensures that all subsequent work with the data uses the improved headers, reducing confusion and manual corrections.