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.

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 Python, …

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 can be used.

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

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

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

NumPy provides an N-dimensional array type, the ndarray, which describes a collection of “items” of the same type. The items can be indexed using for example N integers.

Jul 8, 2025 · Numerical computing tools NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

numpy.reshape # numpy.reshape(a, /, shape=None, order='C', *, newshape=None, copy=None) [source] # Gives a new shape to an array without changing its data. Parameters: aarray_like Array to …