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.

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

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 …

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.

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

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

numpy.save # numpy.save(file, arr, allow_pickle=True, fix_imports=<no value>) [source] # Save an array to a binary file in NumPy .npy format. Parameters: filefile, str, or pathlib.Path File or filename to …

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it.