Enhanced Python Data Processing and Efficiency

📅 2023-01-23 — Session: Enhanced Python Data Processing and Efficiency

🕒 15:50–17:55
🏷️ Labels: Python, Data Processing, Efficiency, Dask, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to improve the clarity and efficiency of Python code used for data processing, particularly with pandas and Dask, and to encapsulate data operations into reusable functions.

Key Activities

• Explored strategies for improving code clarity and efficiency in Python, focusing on descriptive variable names, comments, and pandas methods.
• Developed code to process unemployment rate data, creating new columns and filtering data.
• Demonstrated file retrieval and date extraction from file names using glob, os.scandir(), and regular expressions.
• Reviewed and enhanced functions like ajustar_empleo() and predict_save() for data manipulation and prediction.
• Discussed key questions for data analytics in a Java programming context.
• Provided insights on optimizing Pandas DataFrame operations and encapsulating data processing functions.
• Improved DataFrame merge functions for better readability and performance.
• Applied Dask for sampling, merging, and optimizing large dataset operations, demonstrating delayed computation and multi-core processing.

Achievements

• Improved code clarity and efficiency in data processing tasks.
• Enhanced understanding and application of Dask for large datasets.
• Developed reusable functions for data processing and analysis.

Pending Tasks

• Further optimize functions for specific use cases in data processing.
• Explore additional Dask capabilities for performance enhancement.