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        "author": "Andrii Gakhov",
        "author_email": "andrii.gakhov@gmail.com",
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            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": "PDSA: Probabilistic Data Structures and Algorithms in Python\n************************************************************\n\n.. image:: https://img.shields.io/travis/gakhov/pdsa/master.svg?style=flat-square\n    :target: https://travis-ci.org/gakhov/pdsa\n    :alt: Travis Build Status\n\n.. image:: https://img.shields.io/github/release/gakhov/pdsa/all.svg?style=flat-square\n    :target: https://github.com/gakhov/pdsa/releases\n    :alt: Current Release Version\n\n.. image:: https://img.shields.io/pypi/v/pdsa.svg?style=flat-square\n    :target: https://pypi.python.org/pypi/pdsa\n    :alt: pypi Version\n\n.. image:: https://readthedocs.org/projects/pdsa/badge/?version=latest&style=flat-square\n    :target: http://pdsa.readthedocs.io/en/latest/\n    :alt: Documentation Version\n\n.. image:: https://img.shields.io/pypi/pyversions/pdsa.svg?style=flat-square\n    :target: https://github.com/gakhov/pdsa\n    :alt: Python versions\n\n\n\n.. contents ::\n\nThe Book\n----------\n\nEverybody interested in learning more about probabilistic data structures and\nalgorithms could be referred to our recently published book:\n\n`Probabilistic Data Structures and Algorithms for Big Data Applications <https://pdsa.gakhov.com>`_ by Andrii Gakhov\n\n2019, ISBN: 978-3748190486 (paperback) ASIN: B07MYKTY8W (e-book)\n\n\nIntroduction\n------------\n\nProbabilistic data structures is a common name of data structures\nbased on different hashing techniques.\n\nUnlike regular (or deterministic) data structures, they always provide\napproximated answers, but usually with reliable ways to estimate\nthe error probability.\n\nThe potential losses or errors are fully compensated by extremely\nlow memory requirements, constant query time and scaling.\n\nGitHub repository: `<https://github.com/gakhov/pdsa>`_\n\n\nDependencies\n---------------------\n\n* Python 3.5+ (http://python.org/download/)\n* Cython 0.28+ (http://cython.org/#download)\n\n\nDocumentation\n--------------\n\nThe latest documentation can be found at `<http://pdsa.readthedocs.io/en/latest/>`_\n\n\n**Membership problem**\n\n- `Bloom Filter <http://pdsa.readthedocs.io/en/latest/membership/bloom_filter.html>`_\n- `Counting Bloom Filter <http://pdsa.readthedocs.io/en/latest/membership/counting_bloom_filter.html>`_\n\n**Cardinality problem**\n\n- `Linear counter <http://pdsa.readthedocs.io/en/latest/cardinality/linear_counter.html>`_\n- `Probabilistic counter (Flajolet\u2013Martin algorithm) <http://pdsa.readthedocs.io/en/latest/cardinality/probabilistic_counter.html>`_\n- `HyperLogLog <http://pdsa.readthedocs.io/en/latest/cardinality/hyperloglog.html>`_\n\n**Frequency problem**\n\n- `Count Sketch <http://pdsa.readthedocs.io/en/latest/frequency/count_sketch.html>`_\n- `Count-Min Sketch <http://pdsa.readthedocs.io/en/latest/frequency/count_min_sketch.html>`_\n\n**Rank problem**\n\n- `q-digest <http://pdsa.readthedocs.io/en/latest/rank/qdigest.html>`_\n\n\nLicense\n-------\n\nMIT License\n\n\nSource code\n-----------\n\n* https://github.com/gakhov/pdsa/\n\n\nAuthors\n-------\n\n* Maintainer: `Andrii Gakhov <andrii.gakhov@gmail.com>`\n\n\nInstall with pip\n--------------------\n\nInstallation requires a working build environment.\n\nUsing pip, PDSA releases are currently only available as source packages.\n\n.. code:: bash\n\n    $ pip3 install -U pdsa\n\nWhen using pip it is generally recommended to install packages in a ``virtualenv``\nto avoid modifying system state:\n\n.. code:: bash\n\n    $ virtualenv .env -p python3 --no-site-packages\n    $ source .env/bin/activate\n    $ pip3 install -U cython\n    $ pip3 install -U pdsa\n\n\nCompile from source\n---------------------\n\nThe other way to install PDSA is to clone its\n`GitHub repository <https://github.com/gakhov/pdsa>`_ and build it from\nsource.\n\n.. code:: bash\n\n    $ git clone https://github.com/gakhov/pdsa.git\n    $ cd pdsa\n\n    $ make install\n\n    $ bin/pip3 install -r requirements-dev.txt\n    $ make test",
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