Optimized Data Processing and Performance Measurement in Python

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📅 2023-02-13 — Session: Optimized Data Processing and Performance Measurement in Python

🕒 16:00–18:50
🏷️ Labels: Python, Data Processing, Performance Measurement, Optimization, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to enhance data processing efficiency and performance measurement in Python, focusing on serialization, data manipulation, and optimization techniques.

Key Activities

  • Data Serialization: Implemented methods to save and load Python dictionaries using pickle and json modules, highlighting differences in data type handling.
  • Data Processing: Merged Python scripts for CSV processing using Pandas, optimizing code by removing unnecessary loops and enhancing data reading efficiency.
  • DataFrame Manipulation: Simplified aggregation operations in Pandas DataFrames and addressed NaN indexing issues using str.contains.
  • Performance Measurement: Developed scripts to measure CSV read times and memory usage, utilizing Python’s time library and memory_profiler, and explored execution time measurement using VS Code debugger and a custom time measurement decorator.
  • Linux Troubleshooting: Reflected on SquashFS errors and kernel panic issues in Linux, proposing potential hardware and software solutions.

Achievements

  • Successfully optimized data processing scripts, improving runtime efficiency and resource usage.
  • Developed robust performance measurement tools, aiding in code optimization and debugging.
  • Resolved DataFrame indexing errors, enhancing data manipulation reliability.

Pending Tasks

  • Further exploration of Linux troubleshooting techniques, particularly in resolving SquashFS errors and kernel panic situations.
  • Continued refinement of performance measurement scripts to include more detailed analytics and reporting.

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