Description
SciPy (pronounced "Sigh Pie") is open-source software for mathematics,
science, and engineering. It includes modules for statistics, optimization,
integration, linear algebra, Fourier transforms, signal and image processing,
ODE solvers, and more. It is also the name of a very popular conference on
scientific programming with Python.
The SciPy library depends on NumPy, which provides convenient and fast
N-dimensional array manipulation. The SciPy library is built to work with
NumPy arrays, and provides many user-friendly and efficient numerical routines
such as routines for numerical integration and optimization. Together, they
run on all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be depended
upon by some of the world's leading scientists and engineers. If you need to
manipulate numbers on a computer and display or publish the results, give
SciPy a try!
SciPy alternatives and related packages
Based on the "Science and Data Analysis" category
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Numba
Python JIT (just in time) compiler to LLVM aimed at scientific Python by the developers of Cython and NumPy. -
orange
Data mining, data visualization, analysis and machine learning through visual programming or Python scripting. -
Interactive Parallel Computing with IPython
Interactive Parallel Computing in Python -
bcbio-nextgen
A toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. -
PyDy
Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib. -
Open Babel
A chemical toolbox designed to speak the many languages of chemical data.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
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Recently added SciPy resources
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This blog post shows an example of Jupyter Notebooks embeded in a static HTML with interactivity enabledArticle Added by ELC // elc.github.io // 3 months ago
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As a beginner, you may be looking for a way to get a solid understanding of machine learning that’s not only rigorous and practical, but also concise and fast.. Give this article a read...Article Added by sakimjan // sinxloud.com // 3 months ago
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