NumPy Description

The NumPy vectorization and indexing concepts are fast and flexible. They are the current de-facto standard in array computing. NumPy provides comprehensive mathematical functions, random numbers generators, linear algebra routines and Fourier transforms. NumPy is compatible with a wide variety of hardware and computing platforms. It also works well with sparse array libraries, distributed, GPU, or GPU. NumPy's core is C code that has been optimized. Enjoy Python's flexibility with the speed and efficiency of compiled code. NumPy's high-level syntax makes it easy for programmers of all backgrounds and experience levels. NumPy brings the computational power and simplicity of languages such as C and Fortran into Python, making it a language that is much easier to learn and to use. This power is often accompanied by simplicity: NumPy solutions are often simple and elegant.

NumPy Alternatives
h5py Reviews
h5py
The h5py package provides a Pythonic interface for the HDF5 binary data format. It allows you to store large amounts of numerical data and allow you to easily manipulate that data using NumPy. You can cut into multi-terabyte datasets on disk as if they were NumPy arrays. You can store thousands of datasets in one file. You can categorize and tag them however you like. H5py makes use of simple NumPy and Python metaphors like dictionary syntax and NumPy array syntax. You can, for example, iterate over files or examine the.shape and.dtype attributes of data. To get started, you don't need any knowledge about HDF5. In addition to an easy-to-use interface, h5py relies on an object-oriented Cython wrap of the HDF5 CAP API. You can do almost anything from C in HDF5 with h5py.
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Cython Reviews
Cython
Cython, an optimizing static compiler, is available for both the Python programming languages and the extended Cython programming languages (based on Pyrex). It makes it as easy to write Python extensions using C. Cython combines the power of Python with C, allowing you to write Python code that calls back to C or C++ natively at any time. Static type declarations can be used to convert readable Python code into plain C performance. Combine source code level debugging to identify bugs in Python, Cython and C code. Large data sets can be interacted with efficiently, e.g. Multi-dimensional NumPy arrays. You can quickly build your applications in the mature, well-used CPython ecosystem. The Cython language, which is a superset Python language, supports calling C functions as well as declaring C types on variables or class attributes.
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CVXOPT Reviews
CVXOPT
CVXOPT, a free software package that allows convex optimization using the Python programming language, is available for download. It can be used with Python's interactive interpreter, the command line to execute Python scripts or integrated into other software via Python extension module. Its primary purpose is to simplify the development of software for convex optimization by using Python's extensive standard library as well as the strengths of Python, a high-level programming languages. Efficient Python classes that can handle dense and sparse (real and complicated) matrices. Includes Python indexing, slicing, and overloaded operations for matrix mathmetic. Interfaces to the linear programming solution in GLPK, semidefinite programming solvers DSDP5 and linear, quadratic, and second-order cone programming solutions in MOSEK.
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PyQtGraph Reviews
PyQtGraph
PyQtGraph is a pure Python graphics and GUI library based on PyQt/PySide or NumPy. It is intended for use in mathematics/scientific/engineering applications. Although written entirely in Python, the library is extremely fast thanks to its heavy use of NumPy to perform number crunching and Qt GraphicsView framework for fast display. PyQtGraph is available under the MIT open source license. Interactive view boxes for basic 2D plotting. Line and scatter plots. Data can be scaled/panned with the mouse. Rapid drawing allows for real-time data display. Displays all data types (int, float, any bit depth, RGB, RGBA or luminance). Multidimensional images can be sliced at arbitrary angles using these functions (great for MRI data). Rapid update for video display and real-time interaction Image display with interactive level control and lookup tables. Mesh rendering with isosurface generator. Interactive viewports can be rotated/zoomed with the mouse. For easier programming, use the basic 3D scenegraph.
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Pricing

Pricing Starts At:
Free
Free Version:
Yes

Integrations

API:
Yes, NumPy has an API
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Reviews - 1 Verified Review

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Company Details

Company:
NumPy
Website:
numpy.org

Media

NumPy Screenshot 1
NumPy Screenshot 1
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Product Details

Platforms
Windows
Mac
Linux
Type of Training
Documentation
Videos
Customer Support
Online

NumPy Features and Options

NumPy User Reviews

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  • Name: Anonymous (Verified)
    Job Title: Principal Software Engineer
    Length of product use: 6-12 Months
    Used How Often?: Daily
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Excellent Python math library

    Date: Aug 03 2022

    Summary: NumPy is an essential part of the Python ecosystem. It provides a huge variety of mathematical functions in a very performant library for free.

    Positive: - used for scientific computing
    - huge variety of mathematical functions, random number generators, etc.
    - supports a wide variety of hardware and GPU acceleration
    - very fast code that runs in C, despite working with Python
    - simple syntax makes it easy to learn
    - good documentation
    - free and open source

    Negative: - there aren't many cons to using NumPy; it's a mainstay of the Python computing community for a reason

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