📅 2024-11-18 — Session: Enhanced Data Processing and Visualization in Python
🕒 17:20–18:30
🏷️ Labels: Python, Pandas, Data Processing, Visualization, ETL, Styling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary objective of this session was to enhance data processing and visualization techniques in Python, focusing on error handling, time index management, ETL processes, and styling for reports.
Key Activities
- Fixing Summation Error: Addressed errors in summing DataFrame columns with non-numeric types by filtering numeric columns.
- Time Index Management: Managed time indices in Pandas DataFrames for efficient data slicing.
- Data Loading and Preprocessing: Developed a robust data loading and preprocessing pipeline using Pandas.
- Updated ETL Flow: Incorporated currency management in ETL processes to handle USD and ARS scenarios.
- Styling Enhancements: Improved styling for monthly and ledger reports using Seaborn, focusing on color coding and bolding.
- Modular Styling Functions: Implemented modular functions for styling DataFrames, enhancing code clarity and maintainability.
- Compatibility Updates: Updated styling code for compatibility with newer Pandas versions.
Achievements
- Resolved data processing errors and improved the robustness of ETL processes.
- Enhanced visualization techniques for better data presentation.
Pending Tasks
- Further testing and validation of the updated ETL flow and styling functions.