How can I improve the performance of a Python application on Windows?

For beginners, focus on measuring performance first, then make small improvements in the areas that matter most.

Recommended practices:

1. Measure before optimizing
   • Performance includes CPU usage, memory consumption, power consumption, network and storage utilization, and animation performance.
   • Make performance a regular part of development and testing instead of waiting until the app feels slow.
2. Use profiling to find where time is spent
   • In Visual Studio, Python profiling is available for Python 3.9 and earlier with a CPython-based interpreter.
   • To profile code in Visual Studio:
     1. Open the Python code file.
     2. Confirm the interpreter is CPython-based.
     3. Select Debug > Launch Python Profiling.
     4. Choose either the whole project or a standalone script.
     5. Select Start and review the performance report.
   • This helps identify which functions or parts of the program consume the most time.
3. Reduce memory usage
   • Lower memory usage improves app performance and overall system responsiveness.
   • Good beginner habits include:
     • reducing foreground memory usage,
     • minimizing background work,
     • releasing resources while the app is in the background,
     • avoiding memory leaks.
   • Dynamic memory is the more significant source of memory usage and where leaks usually appear.
4. Benchmark without debugger overhead
   • When comparing performance, run Python code with Debug > Start Without Debugging or Ctrl+F5.
   • This avoids debugger overhead and gives a more realistic benchmark.
5. Keep Python code correct and simple before optimizing
   • Write the solution in pure Python first.
   • After the code is correct, measure it and then optimize the slow parts.
6. Consider native extensions only after profiling
   • If a specific computation-heavy section is proven to be the bottleneck, a C++ extension can significantly improve speed.
   • In the documented example, C++ routines ran about 5 to 20 times faster than the Python implementation.
   • Also note that a debug build of a C++ module runs slower than a release build.
7. Use a solid Windows Python setup
   • A basic setup for learning on Windows is Python, Visual Studio Code, and the Python extension for VS Code.
   • VS Code also provides Python support for editing, linting, debugging, and unit testing.

A simple beginner workflow is:

• write the program in pure Python,
• run it without debugging for timing,
• profile it if it is slow,
• reduce unnecessary memory use and background work,
• optimize only the parts proven to be slow.

• Windows app performance and fundamentals overview
• Improve app performance by reducing the use of memory and disk space
• Profile Python code in Visual Studio
• Create a C++ extension for Python in Visual Studio
• Create a C++ extension for Python in Visual Studio
• Set up your Python development environment on Windows
• Set up your Python development environment on Windows