What affects the performance of the Python programming language?

Several factors influence Python performance:

• Interpreter: The standard CPython is slower than compiled languages because it executes bytecode interpretively.

• Global Interpreter Lock (GIL): Limits the parallel execution of threads, reducing the efficiency of multithreading.

• Data types and structures: Using inefficient data structures or frequent operations with large objects slow down performance.

• External libraries: Using optimized libraries (e.g., NumPy) can significantly speed up computations.

• Algorithms and code: Inefficient code and algorithms have the most significant impact.

• Input/output: Disk and network operations can become bottlenecks.

To improve performance, often used techniques include:

• Compilation to Cython or PyPy
• Multiprocessing instead of multithreading
• Optimization of algorithms and using native extensions.