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 …

NumPy numerical types are instances of numpy.dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using import numpy as np you can create …

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

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 reference Routines and objects by topic Array manipulation routines numpy.append

That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones. Create an array of the given shape and populate …

NumPy is flexible, and ndarray objects can accommodate any strided indexing scheme. In a strided scheme, the N-dimensional index (n 0, n 1,, n N 1) corresponds to the offset (in bytes):

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

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