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 learn …

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 can be used.

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 Python, …

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

NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers.

NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference.

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

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 + ... + p[deg] of …

NumPy numerical types are instances of numpy.dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using import numpy as np you can create arrays …