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 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 …

NumPy 1.20 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.18 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 is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries.

Handling complex numbers # ... Extrema finding # ... Miscellaneous # ... previous numpy.not_equal next numpy.sin

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

These documents clarify concepts, design decisions, and technical constraints in NumPy. This is a great place to understand the fundamental NumPy ideas and philosophy.

Notes Broadcasting rules apply, see the numpy.linalg documentation for details. The eigenvalues/eigenvectors are computed using LAPACK routines _syevd, _heevd. The …