Refactored Python scripts for data processing

• Day: 2023-12-20
• Time: 18:00 to 21:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Dask, Data Processing, Optimization, Code Refactoring

Description

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.

Evidence

• source_file=2023-12-20.sessions.jsonl, line_number=3, event_count=0, session_id=b65f612a9ff93dcdf6ed6e1e57ddc6e324e204458a9995ce41821da4b1b0eebc
• event_ids: []