📅 2024-11-18 — Session: Enhanced DataFrame Operations and Styling
🕒 17:20–18:25
🏷️ Labels: Python, Pandas, Dataframe, Styling, ETL, Visualization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance DataFrame operations and styling techniques in Python using Pandas and Seaborn, focusing on error handling, data preprocessing, ETL processes, and visualization improvements.
Key Activities
- Fixing Summation Error: Addressed an error in summing DataFrame columns by filtering numeric types to exclude
datetime64. - Time Index Management: Implemented efficient time index management by setting ‘Date’ as the index and using a ‘YearMonth’ helper column.
- Data Loading and Preprocessing: Developed a robust script for data loading and preprocessing, including dynamic column naming and safe column dropping.
- Updated ETL Flow: Enhanced the ETL process with currency management for USD and ARS, ensuring seamless conversion and calculations.
- Styling Enhancements: Updated styling for monthly and ledger reports using Seaborn, focusing on color coding and bolding for emphasis.
- Modular Styling Functions: Created modular functions for styling DataFrames, improving code clarity and maintainability.
- Compatibility Updates: Updated styling code to replace deprecated
.applymap()with.map()for compatibility with newer Pandas versions.
Achievements
- Successfully fixed DataFrame summation errors and improved time index management.
- Developed a comprehensive data preprocessing pipeline.
- Enhanced ETL processes with effective currency management.
- Improved report styling with modular and compatible code.
Pending Tasks
- Further testing of the updated ETL flow with larger datasets to ensure performance and accuracy.
- Review and optimization of the modular styling functions for broader use cases.