How can I use NumPy to improve performance in my Python code?

How can I use NumPy to improve performance in my Python code?

You can use NumPy to optimize performance by leveraging its array operations, which are implemented in C and are much faster than Python lists.

Can you give me an example of using NumPy for matrix operations?

Sure! For example, you can create two matrices using NumPy and perform matrix multiplication like this:

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
C = np.dot(A, B)
print(C)

What are some common pitfalls when using NumPy?

Common pitfalls include not using vectorized operations, which can lead to slower performance, and misunderstanding broadcasting rules.

How does NumPy handle large datasets?

NumPy is designed to handle large datasets efficiently by using contiguous memory blocks and optimized algorithms for array operations.