Numerical computing tools NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

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 …

Versions: NumPy 2.3 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2.2 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2.1 Manual [HTML+zip] …

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 reference Routines and objects by topic Mathematical functions numpy.power

NumPy reference Routines and objects by topic Array manipulation routines numpy.reshape

Linear algebra # The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient low level implementations of standard linear algebra algorithms.

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 + ...

Highlights NumPy 2.0 Python API removals Deprecations Expired deprecations Compatibility notes C API changes NumPy 2.0 C API removals New Features Improvements Changes …