📅 2023-04-19 — Session: Enhanced DataFrame Parsing and Error Handling Functions
🕒 19:05–19:25
🏷️ Labels: Python, Dataframe, Data Parsing, Error Handling, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session focused on developing and refining Python functions for advanced DataFrame parsing and error handling, specifically targeting ‘theme’, ‘sector’, and advertising sector columns.
Key Activities
- Developed a function to parse ‘theme’ columns in a DataFrame, splitting them into three separate columns based on a specified delimiter.
- Enhanced the
parse_theme_columns()function to handle column name mismatches and fill missing values, ensuring robustness in data parsing. - Created a function to count unique values in a specified DataFrame column, returning results as a dictionary.
- Implemented a function to count parseable pieces in a DataFrame column using a custom delimiter, with example usage and limitations.
- Updated the
parse_theme_columns()function to handle null values effectively, applying operations only to non-null entries. - Developed functions to parse ‘ad_sector_codes’, ‘ad_sector_names’, ‘sector’, and ‘mjsector’ columns, splitting values and creating indexed suffix columns.
Achievements
- Successfully created and refined multiple Python functions for parsing and error handling in DataFrames, improving data processing capabilities.
- Addressed common data parsing issues such as null values, column mismatches, and whitespace trimming, enhancing data integrity.
Pending Tasks
- Further testing of the new functions with large datasets to ensure performance and scalability.
- Integration of these functions into existing data processing pipelines to streamline operations.