Metadata-Version: 2.1
Name: statstream
Version: 19.1.0
Summary: Statistics for Streaming Data
Home-page: https://github.com/jmaces/statstream
Author: Jan Maces
Author-email: janmaces@gmail.com
Maintainer: Jan Maces
Maintainer-email: janmaces@gmail.com
License: MIT
Description: =============================================
        ``statstream``: Statistics for Streaming Data
        =============================================
        
        .. add project badges here
        .. image:: https://readthedocs.org/projects/statstream/badge/?version=latest
            :target: https://statstream.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
        .. image:: https://travis-ci.com/jmaces/statstream.svg?branch=master
            :target: https://travis-ci.com/jmaces/statstream?branch=master
            :alt: CI Status
        
        .. image:: https://codecov.io/gh/jmaces/statstream/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/jmaces/statstream
          :alt: Code Coverage
        
        .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
            :target: https://github.com/psf/black
            :alt: Code Style: Black
        
        
        .. teaser-start
        
        ``statstream`` is a lightweight Python package providing data analysis and statistics utilities for streaming data.
        
        Its main goal is to provide **single-pass** variants of conventional `numpy <https://numpy.org/>`_
        data analysis and statistics functionality for **streaming** data that is
        either generated on the fly or to large to be handled at once. Data can be
        streamed as in chunks called **mini-batches**, which makes ``statstream``
        extremely useful in combination with machine learning and deep learning
        packages like `keras <https://keras.io/>`_, `tensorflow <https://www.tensorflow.org/>`_, or `pytorch <https://pytorch.org/>`_.
        
        .. teaser-end
        
        
        .. example
        
        ``statstream`` functions consume iterators providing batches of data.
        They compute statistics of these batches and combine them to obtain statistics
        for the full data set.
        
        .. code-block:: python
        
           import statstream
           mean = statstream.streaming_mean(some_iterable)
        
        The `Overview <https://statstream.readthedocs.io/en/latest/overview.html>`_ and
        `Examples <https://statstream.readthedocs.io/en/latest/examples.html>`_ sections
        of our documentation provide more realistic and complete examples.
        
        .. project-info-start
        
        Project Information
        ===================
        
        ``statstream`` is released under the `MIT license <https://github.com/jmaces/statstream/blob/master/LICENSE>`_,
        its documentation lives at `Read the Docs <https://statstream.readthedocs.io/en/latest/>`_,
        the code on `GitHub <https://github.com/jmaces/statstream>`_,
        and the latest release can soon be found on PyPI.
        It’s tested on Python 2.7 and 3.4+.
        
        If you'd like to contribute to ``statstream`` you're most welcome.
        We have written a `short guide <https://github.com/jmaces/statstream/blob/master/.github/CONTRIBUTING.rst>`_ to help you get you started!
        
        .. project-info-end
        
        
        .. literature-start
        
        Further Reading
        ===============
        
        Additional information on the algorithmic aspects of ``statstream`` can be found
        in the following works:
        
        - Tony F. Chan & Gene H. Golub & Randall J. LeVeque,
          “Updating formulae and a pairwise algorithm for computing sample variances”,
          1979
        - Radim, Rehurek,
          “Scalability of Semantic Analysis in Natural Language Processing”,
          2011
        
        .. literature-end
        
        
        Acknowledgments
        ===============
        
        During the setup of this project we were heavily influenced and inspired by
        the works of `Hynek Schlawack <https://hynek.me/>`_ and in particular his
        `attrs <https://www.attrs.org/en/stable/>`_ package and blog posts on
        `testing and packaing <https://hynek.me/articles/testing-packaging/>`_
        and `deploying to PyPI <https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/>`_.
        Thank you for sharing your experiences and insights.
        
Keywords: statistics,data-science,streaming-data,numpy
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Description-Content-Type: text/x-rst
Provides-Extra: docs
Provides-Extra: tests
Provides-Extra: dev
