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! You can use numpy.dot() for matrix multiplication. For example:

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

What about using NumPy for large datasets?

NumPy is great for large datasets because it uses contiguous memory blocks, which reduces overhead and speeds up operations. You can also use functions like numpy.mean() or numpy.sum() to perform calculations efficiently.

Are there any alternatives to NumPy for performance?

Yes, alternatives include libraries like CuPy for GPU acceleration, or Dask for parallel computing with larger-than-memory datasets.