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 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 …

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

NumPy 1.19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.18 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.17 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.

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 …

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

Handling complex numbers # ... Extrema finding # ... Miscellaneous # ... numpy.not_equal

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.