Nearly every scientist working in Python draws on the power of NumPy. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to …

The reference guide contains a detailed description of the functions, modules, and objects included in NumPy. The reference describes how the methods work and which parameters …

The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes …

The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data …

Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community.

What is NumPy? # NumPy is the fundamental package for scientific computing in Python.

NumPy reference Routines and objects by topic Mathematical functions numpy.power

numpy.where # numpy.where(condition, [x, y, ]/) # Return elements chosen from x or y depending on condition.

Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide. Fit a polynomial p(x) = p[0] * x**deg + ...

The native NumPy indexing type is intp and may differ from the default integer array type. intp is the smallest data type sufficient to safely index any array; for advanced indexing it may be …