Manipulate JSON-like data with NumPy-like idioms. uarray: Python backend system that decouples API from implementation; unumpy provides a NumPy API. tensorly: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy.

Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. Beginners# There’s a ton of information about NumPy out there. If you are just starting, we’d strongly recommend the following: Tutorials. NumPy Quickstart Tutorial

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. It assumes that you have an understanding of the key concepts.

2024年12月14日 · This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation . Python API #

NumPy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typically, such operations are executed more efficiently and with less code than is possible using Python’s built-in sequences.

NumPy can be installed with conda, with pip, with a package manager on macOS and Linux, or from source. For more detailed instructions, consult our Python and NumPy installation guide below.

NumPy (Numerical Python) is an open source Python library that’s widely used in science and engineering. The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate

NumPy Documentation. Web; Latest (development) documentation; NumPy Enhancement Proposals; Versions: Numpy 2.2 Manual [Reference Guide PDF] [User Guide PDF] Numpy 2.1 Manual [Reference Guide PDF] [User Guide PDF] Numpy 2.0 Manual [Reference Guide PDF] [User Guide PDF] Numpy 1.26 ...

NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions” (ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>>

The 1D array creation functions e.g. numpy.linspace and numpy.arange generally need at least two inputs, start and stop. numpy.arange creates arrays with regularly incrementing values. Check the documentation for complete information and examples.