How do different Python performance libraries compare?

How do different Python performance libraries compare?

There are several performance libraries in Python, such as NumPy, Cython, and Numba. Each has its strengths depending on the use case.

Can you explain the main differences between NumPy and Cython?

NumPy is primarily for numerical computations and provides a powerful array object, while Cython is used to compile Python code to C for performance improvements.

What about Numba? How does it fit into the picture?

Numba is a just-in-time compiler that translates a subset of Python and NumPy code into fast machine code, making it great for speeding up numerical functions.

Which one should I use for data analysis?

For data analysis, NumPy is often the go-to choice due to its extensive functionality and ease of use, but Numba can be beneficial for optimizing specific performance-critical functions.