What is NumPy?
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
NumPy is a tool in the Data Science Tools category of a tech stack.
NumPy is an open source tool with 12.5K GitHub stars and 4.1K GitHub forks. Here’s a link to NumPy's open source repository on GitHub
Who uses NumPy?
101 companies reportedly use NumPy in their tech stacks, including Instacart, SendGrid, and SweepSouth.
295 developers on StackShare have stated that they use NumPy.
Python, Theano, Dask, Chainer, and PyXLL are some of the popular tools that integrate with NumPy. Here's a list of all 8 tools that integrate with NumPy.
Why developers like NumPy?
Here’s a list of reasons why companies and developers use NumPy
Here are some stack decisions, common use cases and reviews by companies and developers who chose NumPy in their tech stack.
We utilize NumPy, SciPy, Pandas, and iPython Notebooks to power our analysis and analytics tools. NumPy
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
NumPy Alternatives & Comparisons
What are some alternatives to NumPy?
See all alternatives
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.