SciPy
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.
SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. It builds on the NumPy extension and provides a large number of higher-level functions that operate on numpy arrays and are useful for different types of scientific and engineering applications.
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Website Key Features
NumPy
Fundamental package for scientific computing with Python. It provides support for arrays, matrices, and many mathematical functions.
Matplotlib
A comprehensive library for creating static, animated, and interactive visualizations in Python.
Pandas
Provides high-performance, easy-to-use data structures and data analysis tools.
SymPy
A Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible.
IPython
An interactive command-line interface for Python, offering enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.
SciPy Library
A collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.
scikit-learn
A simple and efficient tool for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib.
statsmodels
A Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
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Additional information
License
BSD License
Community
SciPy has a vibrant community of developers and users. It hosts annual conferences and has a mailing list for discussions.
Documentation
Extensive documentation is available for all the libraries within the SciPy ecosystem, making it easier for new users to get started.
Contributing
SciPy welcomes contributions from the community. There are guidelines available for those who wish to contribute code, documentation, or even report bugs.
Development
The development of SciPy is ongoing, with regular updates and new features being added. The development process is open and transparent, with discussions taking place on public mailing lists and GitHub.
HTTP headers
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