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 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.

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 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] [Reference …

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

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.

The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so …

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

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 faster than …