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        "author": "Ran Aroussi",
        "author_email": "ran@aroussi.com",
        "bugtrack_url": null,
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            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Database",
            "Topic :: Database :: Database Engines/Servers",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "PyStore - Fast data store for Pandas timeseries data\n====================================================\n\n.. image:: https://img.shields.io/badge/python-2.7,%203.5+-blue.svg?style=flat\n    :target: https://pypi.python.org/pypi/pystore\n    :alt: Python version\n\n.. image:: https://img.shields.io/pypi/v/pystore.svg?maxAge=60\n    :target: https://pypi.python.org/pypi/pystore\n    :alt: PyPi version\n\n.. image:: https://img.shields.io/pypi/status/pystore.svg?maxAge=60\n    :target: https://pypi.python.org/pypi/pystore\n    :alt: PyPi status\n\n.. image:: https://img.shields.io/travis/ranaroussi/pystore/master.svg?maxAge=1\n    :target: https://travis-ci.org/ranaroussi/pystore\n    :alt: Travis-CI build status\n\n.. image:: https://www.codefactor.io/repository/github/ranaroussi/pystore/badge\n    :target: https://www.codefactor.io/repository/github/ranaroussi/pystore\n    :alt: CodeFactor\n\n.. image:: https://img.shields.io/github/stars/ranaroussi/pystore.svg?style=social&label=Star&maxAge=60\n    :target: https://github.com/ranaroussi/pystore\n    :alt: Star this repo\n\n.. image:: https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60\n    :target: https://twitter.com/aroussi\n    :alt: Follow me on twitter\n\n\\\n\n\n`PyStore <https://github.com/ranaroussi/pystore>`_ is a simple (yet powerful)\ndatastore for Pandas dataframes, and while it can store any Pandas object,\n**it was designed with storing timeseries data in mind**.\n\nIt's built on top of `Pandas <http://pandas.pydata.org>`_, `Numpy <http://numpy.pydata.org>`_,\n`Dask <http://dask.pydata.org>`_, and `Parquet <http://parquet.apache.org>`_\n(via `Fastparquet <https://github.com/dask/fastparquet>`_),\nto provide an easy to use datastore for Python developers that can easily\nquery millions of rows per second per client.\n\n\n==> Check out `this Blog post <https://medium.com/@aroussi/fast-data-store-for-pandas-time-series-data-using-pystore-89d9caeef4e2>`_\nfor the reasoning and philosophy behind PyStore, as well as a detailed tutorial with code examples.\n\n==> Follow `this PyStore tutorial <https://github.com/ranaroussi/pystore/blob/master/examples/pystore-tutorial.ipynb>`_ in Jupyter notebook format.\n\n\nQuickstart\n==========\n\nInstall PyStore\n---------------\n\nInstall using `pip`:\n\n.. code:: bash\n\n    $ pip install pystore --upgrade --no-cache-dir\n\nInstall using `conda`:\n\n.. code:: bash\n\n    $ conda install -c ranaroussi pystore\n\n**INSTALLATION NOTE:**\nIf you don't have Snappy installed (compression/decompression library), you'll need to\nyou'll need to `install it first <https://github.com/ranaroussi/pystore#dependencies>`_.\n\n\nUsing PyStore\n-------------\n\n.. code:: python\n\n    #!/usr/bin/env python\n    # -*- coding: utf-8 -*-\n\n    import pystore\n    import quandl\n\n    # Set storage path (optional, default is `~/pystore`)\n    pystore.set_path(\"~/pystore\")\n\n    # List stores\n    pystore.list_stores()\n\n    # Connect to datastore (create it if not exist)\n    store = pystore.store('mydatastore')\n\n    # List existing collections\n    store.list_collections()\n\n    # Access a collection (create it if not exist)\n    collection = store.collection('NASDAQ')\n\n    # List items in collection\n    collection.list_items()\n\n    # Load some data from Quandl\n    aapl = quandl.get(\"WIKI/AAPL\", authtoken=\"your token here\")\n\n    # Store the first 100 rows of the data in the collection under \"AAPL\"\n    collection.write('AAPL', aapl[:100], metadata={'source': 'Quandl'})\n\n    # Reading the item's data\n    item = collection.item('AAPL')\n    data = item.data  # <-- Dask dataframe (see dask.pydata.org)\n    metadata = item.metadata\n    df = item.to_pandas()\n\n    # Append the rest of the rows to the \"AAPL\" item\n    collection.append('AAPL', aapl[100:])\n\n    # Reading the item's data\n    item = collection.item('AAPL')\n    data = item.data\n    metadata = item.metadata\n    df = item.to_pandas()\n\n\n    # --- Query functionality ---\n\n    # Query avaialable symbols based on metadata\n    collection.list_items(some_key='some_value', other_key='other_value')\n\n\n    # --- Snapshot functionality ---\n\n    # Snapshot a collection\n    # (Point-in-time named reference for all current symbols in a collection)\n    collection.create_snapshot('snapshot_name')\n\n    # List available snapshots\n    collection.list_snapshots()\n\n    # Get a version of a symbol given a snapshot name\n    collection.item('AAPL', snapshot='snapshot_name')\n\n    # Delete a collection snapshot\n    collection.delete_snapshot('snapshot_name')\n\n\n    # ...\n\n\n    # Delete the item from the current version\n    collection.delete_item('AAPL')\n\n    # Delete the collection\n    store.delete_collection('NASDAQ')\n\n\nConcepts\n========\n\nPyStore provides namespaced *collections* of data.\nThese collections allow bucketing data by *source*, *user* or some other metric\n(for example frequency: End-Of-Day; Minute Bars; etc.). Each collection (or namespace)\nmaps to a directory containing partitioned **parquet files** for each item (e.g. symbol).\n\nA good practice it to create collections that may look something like this:\n\n* collection.EOD\n* collection.ONEMINUTE\n\nRequirements\n============\n\n* Python >= 3.5\n* Pandas\n* Numpy\n* Dask\n* Fastparquet\n* `Snappy <http://google.github.io/snappy/>`_ (Google's compression/decompression library)\n* multitasking\n\nPyStore was tested to work on \\*nix-like systems, including macOS.\n\n\nDependencies:\n-------------\n\nPyStore uses `Snappy <http://google.github.io/snappy/>`_,\na fast and efficient compression/decompression library from Google.\nYou'll need to install Snappy on your system before installing PyStore.\n\n\\* See the ``python-snappy`` `Github repo <https://github.com/andrix/python-snappy#dependencies>`_ for more information.\n\n***nix Systems:**\n\n- APT: ``sudo apt-get install libsnappy-dev``\n- RPM: ``sudo yum install libsnappy-devel``\n\n**macOS:**\n\nFirst, install Snappy's C library using `Homebrew <https://brew.sh>`_:\n\n.. code::\n\n    $ brew install snappy\n\nThen, install Python's snappy using conda:\n\n.. code::\n\n    $ conda install python-snappy -c conda-forge\n\n...or, using `pip`:\n\n.. code::\n\n    $ CPPFLAGS=\"-I/usr/local/include -L/usr/local/lib\" pip install python-snappy\n\n\n**Windows:**\n\nWindows users should checkout `Snappy for Windows <https://snappy.machinezoo.com>`_ and `this Stackoverflow post <https://stackoverflow.com/a/43756412/1783569>`_ for help on installing Snappy and ``python-snappy``.\n\n\nRoadmap\n=======\n\nPyStore currently offers support for local filesystem.\nI plan on adding support for Amazon S3 (via `s3fs <http://s3fs.readthedocs.io/>`_),\nGoogle Cloud Storage (via `gcsfs <https://github.com/dask/gcsfs/>`_)\nand Hadoop Distributed File System (via `hdfs3 <http://hdfs3.readthedocs.io/>`_) in the future.\n\nAcknowledgements\n================\n\nPyStore is hugely inspired by `Man AHL <http://www.ahl.com/>`_'s\n`Arctic <https://github.com/manahl/arctic>`_ which uses\nMongoDB for storage and allow for versioning and other features.\nI highly reommend you check it out.\n\n\n\nLicense\n=======\n\n\nPyStore is licensed under the **Apache License, Version 2.0**. A copy of which is included in LICENSE.txt.\n\n-----\n\nI'm very interested in your experience with PyStore.\nPlease drop me an note with any feedback you have.\n\nContributions welcome!\n\n\\- **Ran Aroussi**\n",
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