Enhanced Python Data Processing Techniques

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

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

Session Goal

The session aimed to improve the clarity, efficiency, and functionality of Python code used for data processing, particularly focusing on pandas and Dask libraries.

Key Activities

• Discussed strategies for enhancing code clarity and efficiency in Python, including the use of descriptive variable names and comments.
• Demonstrated data processing techniques for unemployment rate analysis using pandas.
• Explored file retrieval methods with glob and os.scandir, and date extraction from filenames using regular expressions.
• Reviewed functions like ajustar_empleo() for adjusting employment data and predict_save() for model predictions.
• Optimized dataframe operations in pandas and encapsulated data processing operations into reusable functions.
• Improved functions for dataframe merging and poverty measurement.
• Enhanced Dask DataFrame performance through sampling, merging, and delayed computation.

Achievements

• Developed and refined multiple Python functions for data manipulation, improving code readability and efficiency.
• Implemented advanced techniques for handling large datasets with Dask, including performance optimization strategies.

Pending Tasks

• Further testing and validation of the new functions in real-world scenarios to ensure robustness and efficiency.
• Explore additional optimization techniques for Dask and pandas to handle even larger datasets.