📅 2023-04-19 — Session: Enhanced DataFrame Parsing Functions for Robust Data Handling
🕒 19:05–19:25
🏷️ Labels: Python, Dataframe, Data Parsing, Pandas, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to enhance and develop Python functions for robust data parsing and manipulation within pandas DataFrames, focusing on handling theme and sector columns.
Key Activities
- Developed a function to parse ‘theme’ columns in a DataFrame into separate columns using a specified delimiter.
- Improved error handling in the
parse_theme_columns()function to manage column mismatches and fill missing values. - Created a function to count unique values in a DataFrame column, returning results as a dictionary.
- Implemented a function to count parseable pieces in a DataFrame column using a custom delimiter.
- Enhanced the
parse_theme_columns()function to handle null values robustly, ensuring parsing only occurs on non-null entries. - Developed a function to parse ‘ad_sector_codes’ and ‘ad_sector_names’, splitting them and creating new columns with suffixes based on sector count.
- Added a function to parse ‘sector’ and ‘mjsector’ columns, creating indexed suffix columns and trimming whitespace from string values.
Achievements
- Successfully developed and refined multiple DataFrame parsing functions, enhancing data manipulation capabilities and robustness.
Pending Tasks
- Further testing and validation of the developed functions with diverse datasets to ensure robustness and adaptability.
- Documentation of the functions and their usage examples for future reference and ease of use.