63 lines
2.2 KiB
ReStructuredText
63 lines
2.2 KiB
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About NumPy
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===========
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NumPy is the fundamental package
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needed for scientific computing with Python. This package contains:
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- a powerful N-dimensional :ref:`array object <arrays>`
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- sophisticated :ref:`(broadcasting) functions <ufuncs>`
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- basic :ref:`linear algebra functions <routines.linalg>`
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- basic :ref:`Fourier transforms <routines.fft>`
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- sophisticated :ref:`random number capabilities <numpyrandom>`
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- tools for integrating Fortran code
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- tools for integrating C/C++ code
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Besides its obvious scientific uses, *NumPy* can also be used as an
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efficient multi-dimensional container of generic data. Arbitrary
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data types can be defined. This allows *NumPy* to seamlessly and
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speedily integrate with a wide variety of databases.
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NumPy is a successor for two earlier scientific Python libraries:
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Numeric and Numarray.
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NumPy community
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---------------
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NumPy is a distributed, volunteer, open-source project. *You* can help
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us make it better; if you believe something should be improved either
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in functionality or in documentation, don't hesitate to contact us --- or
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even better, contact us and participate in fixing the problem.
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Our main means of communication are:
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- `scipy.org website <https://scipy.org/>`__
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- `Mailing lists <https://scipy.org/scipylib/mailing-lists.html>`__
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- `NumPy Issues <https://github.com/numpy/numpy/issues>`__ (bug reports go here)
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- `Old NumPy Trac <http://projects.scipy.org/numpy>`__ (dead link)
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More information about the development of NumPy can be found at our `Developer Zone <https://scipy.scipy.org/scipylib/dev-zone.html>`__.
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The project management structure can be found at our :doc:`governance page <dev/governance/index>`
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About this documentation
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========================
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Conventions
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-----------
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Names of classes, objects, constants, etc. are given in **boldface** font.
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Often they are also links to a more detailed documentation of the
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referred object.
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This manual contains many examples of use, usually prefixed with the
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Python prompt ``>>>`` (which is not a part of the example code). The
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examples assume that you have first entered::
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>>> import numpy as np
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before running the examples.
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