📅 2023-12-20 — Session: Refactored Python scripts for data processing
🕒 18:00–21:10
🏷️ Labels: Python, Dask, Data Processing, Optimization, Code Refactoring
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the readability, modularity, and efficiency of Python scripts used for data processing with Dask and Pandas.
Key Activities
- Refactored existing Python scripts to break down code into smaller, focused functions for improved readability and modularity.
- Simplified data processing scripts by consolidating functionalities and using Dask for handling large datasets.
- Developed scripts for processing ID, VAT degrees, and firm sizes data, saving results to CSV files.
- Addressed errors related to Dask and Pandas operations, ensuring proper computation and error handling.
- Optimized data processing pipelines by experimenting with Dask block sizes and configuring the scheduler for thread management.
- Implemented parallel processing strategies using Dask and concurrent.futures to enhance performance.
- Resolved compatibility issues between Dask and Bokeh for dashboard functionality.
Achievements
- Improved code quality by refactoring and simplifying scripts.
- Enhanced data processing efficiency through optimized Dask configurations and parallel processing.
- Successfully resolved Dask and Pandas compatibility issues.
Pending Tasks
- Further optimization of Dask settings tailored to specific hardware configurations.
- Continued experimentation with Dask block sizes for optimal performance.
- Monitoring and profiling of data processing tasks to identify additional areas for improvement.