There is a setup.py script provided. Typically you run:

python setup.py install

From the directory were you've downloaded the library, and it gets installed automatically.

Consider also more convenient tools that manage installing Python libraries in general like pip or easy_install mentioned in other answers and comments.

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