Optimized Python Code and DataFrame Operations

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📅 2023-03-27 — Session: Optimized Python Code and DataFrame Operations

🕒 21:00–21:30
🏷️ Labels: Python, Optimization, Dataframes, Geospatial, Dynamic Paths
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to enhance the efficiency and performance of Python code, specifically focusing on debugging, optimizing DataFrame operations, and handling geospatial data.

Key Activities

  • Debugging and Optimization: Utilized Python’s built-in libraries such as time, memory_profiler, and pandas_profiling to debug and optimize code performance and memory usage.
  • DataFrame Iteration: Improved DataFrame iteration efficiency by replacing the iterrows() method with apply(), and explored libraries like dask and modin for handling large DataFrames.
  • Data Profiling: Used ProfileReport from the Pandas Profiling library to analyze DataFrames, including generating summary reports for multiple DataFrames.
  • Variable Management: Employed locals() to extract DataFrame names from local variables.
  • Geospatial Data Handling: Fixed coordinate reference system (CRS) issues in GeoDataFrames to ensure compatibility and prevent errors during concatenation.
  • Dynamic File Management: Implemented dynamic path construction and user retrieval using the os module to enhance code flexibility.

Achievements

  • Successfully optimized Python code for better performance and memory management.
  • Enhanced DataFrame processing techniques, leading to more efficient data handling.
  • Resolved CRS issues in geospatial data, facilitating seamless data integration.
  • Improved code flexibility through dynamic path and user management.

Pending Tasks

  • Further exploration of advanced data processing libraries like dask and modin for large-scale DataFrame operations.
  • Continued refinement of geospatial data handling techniques.

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