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.

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

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 structures.

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.

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

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.

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 …

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[0] * x**deg + ... + p[deg] of degree …