SciPy Library: Difference between revisions
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** It can support a [[SciPy Data Structure]], though it relies mostly on [[NumPy's ndarray]]. | ** It can support a [[SciPy Data Structure]], though it relies mostly on [[NumPy's ndarray]]. | ||
** It can be included in a [[Python Distribution]], such as [[Anaconda distribution]]. | ** It can be included in a [[Python Distribution]], such as [[Anaconda distribution]]. | ||
** ... | |||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** [[SciPy v1.2.1]](2019-02-09)[https://github.com/scipy/scipy/releases/tag/v1.2.1] | ** [[SciPy v1.2.1]] (2019-02-09)[https://github.com/scipy/scipy/releases/tag/v1.2.1] | ||
** [[SciPy v0.19.0]](2016-03-09)[https://github.com/scipy/scipy/releases/tag/v0.19.0] | ** [[SciPy v0.19.0]] (2016-03-09)[https://github.com/scipy/scipy/releases/tag/v0.19.0] | ||
** [[SciPy v0.9.0]](2011-02-27)[https://github.com/scipy/scipy/releases/tag/v0.9.0] | ** [[SciPy v0.9.0]] (2011-02-27)[https://github.com/scipy/scipy/releases/tag/v0.9.0] | ||
** http://github.com/scipy/scipy/releases | ** <code>http://github.com/scipy/scipy/releases</code> | ||
** ... | |||
* <B>Counter-Example(s):</B> | * <B>Counter-Example(s):</B> | ||
** [[NumPy Library]]. | ** [[NumPy Library]]. | ||
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== References == | == References == | ||
* http://www.scipy.org/Cookbook/LinearRegression [[Linear Regression]]. | * http://www.scipy.org/Cookbook/LinearRegression [[Linear Regression]]. | ||
* http://www.scipy.org/Cookbook/Solving_Large_Markov_Chains [[Markov Chain]]. | * http://www.scipy.org/Cookbook/Solving_Large_Markov_Chains [[Markov Chain]]. | ||
Latest revision as of 00:39, 6 June 2024
A SciPy Library is an open source Python numerical analysis library.
- Context:
- It can (typically) be composed of SciPy Sub-Modules, such as scipy.stats.
- It can (typically) support SciPy Functions.
- It can support a SciPy Data Structure, though it relies mostly on NumPy's ndarray.
- It can be included in a Python Distribution, such as Anaconda distribution.
- ...
- Example(s):
- SciPy v1.2.1 (2019-02-09)[1]
- SciPy v0.19.0 (2016-03-09)[2]
- SciPy v0.9.0 (2011-02-27)[3]
http://github.com/scipy/scipy/releases- ...
- Counter-Example(s):
- See: Scientific Computing, Matplotlib, SymPy, Scilab, Linear Algebra System.
References
- http://www.scipy.org/Cookbook/LinearRegression Linear Regression.
- http://www.scipy.org/Cookbook/Solving_Large_Markov_Chains Markov Chain.
- http://www.scipy.org/Cookbook/LinearClassification Linear Classification.
2017b
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/SciPy Retrieved:2014-5-9.
- SciPy (pronounced “Sigh Pie”) is a computing environment and open source ecosystem of software for the Python programming language used by scientists, analysts and engineers doing scientific computing and technical computing. SciPy also refers to a specific open source library / Python package of algorithms and mathematical tools that form a core element of the SciPy environment for technical computing. The SciPy environment includes the NumPy and SciPy libraries, along with an expanding set of additional scientific computing libraries like IPython, Matplotlib, pandas and SymPy. It has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The name SciPy is also used by a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India).
SciPy 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.
SciPy is currently distributed under the BSD license, and its development is sponsored by an open community of developers.
- SciPy (pronounced “Sigh Pie”) is a computing environment and open source ecosystem of software for the Python programming language used by scientists, analysts and engineers doing scientific computing and technical computing. SciPy also refers to a specific open source library / Python package of algorithms and mathematical tools that form a core element of the SciPy environment for technical computing. The SciPy environment includes the NumPy and SciPy libraries, along with an expanding set of additional scientific computing libraries like IPython, Matplotlib, pandas and SymPy. It has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The name SciPy is also used by a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India).
2011
- http://www.scipy.org/
- SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. 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!