📅 2023-12-20 — Session: Refactored and Optimized Python Data Processing Scripts
🕒 18:00–21:10
🏷️ Labels: Python, Dask, Data Processing, Optimization, Code Refactoring
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to refactor and optimize Python scripts for data processing, focusing on enhancing readability, modularity, and performance using Dask and Pandas.
Key Activities:
- Refactored Python code to improve readability and modularity by breaking down scripts into smaller functions.
- Simplified data processing scripts by consolidating functionalities and using Dask for handling large datasets.
- Developed scripts for processing ID, VAT degrees, and firm sizes, incorporating Dask for efficient data handling.
- Addressed errors in Dask DataFrame operations and optimized data processing pipelines with Pandas and Dask.
- Conducted experiments to determine optimal block sizes for Dask computations and configured Dask settings for specific hardware.
- Resolved compatibility issues between Dask and Bokeh, ensuring smooth operation of the Dask dashboard.
Achievements:
- Enhanced code readability and maintainability through refactoring.
- Improved data processing efficiency by optimizing Dask configurations and utilizing parallel processing techniques.
- Successfully resolved Dask and Bokeh compatibility issues, enabling effective use of the Dask dashboard.
Pending Tasks:
- Further refine Dask configurations for specific hardware setups to maximize performance.
- Explore additional optimization strategies for Dask workflows, particularly in column renaming and data pipeline execution.