NumPy vs. Scilab Comparison


NumPy	Scilab Scilab Enterprises	+	+
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About Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.	About Numerical analysis or Scientific computing is the study of approximation techniques for numerically solving mathematical problems. Scilab provides graphics functions to visualize, annotate and export data and offers many ways to create and customize various types of plots and charts. Scilab is a high level programming language for scientific programming. It enables a rapid prototyping of algorithms, without having to deal with the complexity of other more low level programming language such as C and Fortran (memory management, variable definition). This is natively handled by Scilab, which results in a few lines of code for complex mathematical operations, where other languages would require much longer codes. It also comes with advanced data structure such as polynomials, matrices and graphic handles and provides an easily operable development environment.
Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Component Library solution for DevOps teams	Audience Statistical Analysis solution for companies
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Supported	API Offers API Not Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing Free Free Version Supported Free Trial Not Supported	Pricing No information available. Free Version Not Supported Free Trial Not Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 3.0 / 5 ease 3.0 / 5 features 3.0 / 5 design 3.0 / 5 support 3.0 / 5 Read all reviews
	Pros & Cons from Real Users Pros The level of compatibility between Scilab, Matlab and Octave is close to 99% which make transitioning from one to another a very easy task. The portability of your codes between these three packages is straightforward. The learning curve is pretty much flat. Scilab is available on all 3 platforms (Windows, Linux, and Mac OS X) Cons Overall, Scilab's performance is less consistent than that of Matlab or Octave in most situations. Of course, it depends on what type of problem you're working on, but for my particular needs, I had always better performance (computational time, memory allocation,...) with Octave than with Scilable. Also, one of the limitation is that Scilab, has much less toolboxes and libraries than its two counterparts. If your looking at basic use of Scilab then you will be fine. But if you're an advanced user, you may feel the limitations.
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Not Supported Webinars Not Supported Live Online Not Supported In Person Supported
Company Information NumPy numpy.org	Company Information Scilab Enterprises www.scilab-enterprises.com
Alternatives Polars	Alternatives Simcenter Compose Siemens
h5py HDF5	MATLAB The MathWorks
broot	MatDeck LabDeck
PyQtGraph	NLREG
Dask View All	CoPlot CoHort Software View All
Categories Component Libraries Supported	Categories Computer-Aided Engineering (CAE) Supported Statistical Analysis Supported
	Show More Features Statistical Analysis Features Analytics Not Supported Association Discovery Supported Compliance Tracking Supported File Management Not Supported File Storage Supported Forecasting Not Supported Multivariate Analysis Not Supported Regression Analysis Not Supported Statistical Process Control Not Supported Statistical Simulation Supported Survival Analysis Not Supported Time Series Not Supported Visualization Supported
Integrations 3LC Supported Avanzai Supported Codédex Supported Coiled Supported Cython Supported Dash Supported Gensim Supported JAX Supported MPI for Python (mpi4py) Supported NVIDIA FLARE Supported PyCharm Supported Spyder Supported Train in Data Supported Unify AI Supported Visual Studio Code Supported Yamak.ai Supported Yandex Data Proc Supported h5py Supported imageio Supported scikit-learn Supported Show More Integrations View All 22 Integrations	Integrations 3LC Not Supported Avanzai Not Supported Codédex Not Supported Coiled Not Supported Cython Not Supported Dash Not Supported Gensim Not Supported JAX Not Supported MPI for Python (mpi4py) Not Supported NVIDIA FLARE Not Supported PyCharm Not Supported Spyder Not Supported Train in Data Not Supported Unify AI Not Supported Visual Studio Code Not Supported Yamak.ai Not Supported Yandex Data Proc Not Supported h5py Not Supported imageio Not Supported scikit-learn Not Supported Show More Integrations View All 1 Integration
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