What methods can be used to optimize Python program performance?

Performance optimization of a Python program can include several approaches:

• Using efficient algorithms and data structures. Replacing an algorithm with a more efficient one often yields a significant improvement.
• Profiling the code. Using modules like cProfile, profile, or line_profiler can help identify bottlenecks.
• Using built-in functions and libraries. They are usually implemented in C and work faster than custom code.
• Minimizing input-output operations. For example, buffering or batch processing data.
• Using generators and iterators. They save memory and speed up processing large data sets.
• Parallelism and multithreading. The multiprocessing module allows utilizing multiple CPU cores.
• Compiling critical sections of code. Using Cython, Numba, or writing extensions in C.

Example of using a generator to save memory:

def squares(n):
    for i in range(n):
        yield i * i

for sq in squares(1000000):
    print(sq)

This approach does not create the entire list at once but generates values as needed.