Optimizing Python Code and DataFrame Handling

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📅 2023-03-27 — Session: Optimizing Python Code and DataFrame Handling

🕒 21:00–21:30
🏷️ Labels: Python, Optimization, Dataframe, Profiling, Geospatial, File Management
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the efficiency of Python code and DataFrame handling, focusing on debugging, optimization, and profiling techniques.

Key Activities

  • Debugging and Optimization: Utilized Python’s built-in libraries like time, memory_profiler, and pandas_profiling to improve code performance and memory usage.
  • DataFrame Iteration: Discussed the inefficiency of iterrows() in Pandas and recommended using apply() for better performance. Explored libraries like dask and modin for handling large DataFrames.
  • Data Profiling: Implemented ProfileReport from Pandas Profiling to analyze DataFrames, including profiling specific columns.
  • Variable Management: Used locals() to extract DataFrame names from local variables.
  • Summary Reports: Generated summary profiling reports for DataFrames using pandas_profiling.
  • Geospatial Data: Fixed CRS issues in GeoDataFrames to ensure proper concatenation.
  • File Management: Demonstrated dynamic path construction using os.path.join() and retrieving the current user’s username with the os module.

Achievements

  • Enhanced understanding and application of Python libraries for debugging and optimization.
  • Improved techniques for efficient DataFrame handling and profiling.
  • Resolved CRS issues in geospatial data processing.

Pending Tasks

  • Further exploration of dask and modin for large-scale DataFrame processing.
  • Implementation of dynamic path and username retrieval in existing projects.

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