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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Concurrent-Pandas\n=================\n\n\nConcurrent Pandas\n-------------\n\n**Concurrent Pandas** is a Python Library that allows you to use Pandas and / or Quandl to concurrently download bulk data using threads or processes. What does concurrency do for you? Download your data simultaneously instead of one key at a time, Concurrent Pandas automatically spawns an optimal number of processes or threads based on the number of processes available on your machine. \n\nNote: Concurrent Pandas is not associated with Quandl or Python Pandas, it just allows you to access them faster. \n\n---\n####Features\n\n- **Working in Python 2 and 3**\n- **Sequential Downloading of Keys**\n- **Concurrent downloading of keys using thread or process pools**\n- **All Concurrent Downloading will automatically pick an optimal number of threads or processes to use for your system**\n- **Recursive data structure unpacking for key insertion**\n  - Pass one or many:\n    - Lists\n    - Sets \n    - Deques\n    - Any other data structures that inherit from abstract base class *Container* provided it is not also inheriting from Python *basestring* and it allows for iteration.\n- **Automatic re-attempts if the download fails or times out**\n  - Retries increase the time to try again with each successive failure\n- **Variety of data sources supported**\n  - Quandl\n  - Federal Reserve Economic Data\n  - Google Finance\n  - Yahoo Finance\n  - More coming soon!\n- **Data is returned in a hashmap for fast lookups** ( *O(1) average case* )\n  - Hash Map Keys are the strings entered for lookup, buckets contain your Panda data frame\n\n\n---\n####Easy to use\n```\n# Define your keys\nyahoo_keys = [\"aapl\", \"xom\", \"msft\", \"goog\", \"brk-b\", \"TSLA\", \"IRBT\"]\n# Instantiate Concurrent Pandas\nfast_panda = concurrentpandas.ConcurrentPandas()\n# Set your data source\nfast_panda.set_source_yahoo_finance()\n# Insert your keys\nfast_panda.insert_keys(yahoo_keys)\n# Choose either asynchronous threads, processes, or a single sequential download\nfast_panda.consume_keys_asynchronous_threads()\n# The Concurrent Pandas object contains a dict of your results now\nmymap = fast_panda.return_map()\n# Easily pull the data out of the map for your research\nprint(mymap[\"aapl\"].head)\n```\n\n---\n#####Installation Instructions\n\nNote : only tested on Linux\n\nTo install execute:\n\n```\npip install ConcurrentPandas\n```\n\n\n---\n#####Updates\n\nNew in 0.1.2\nAbility to interact with stock options\n\nNow requires BeautifulSoup4, and Pandas 0.16 or newer.\n\n---\n#####Misc\n\nTested on Python 2.7.6 and Python 3.4.0 \n\nTo see what else I'm building or follow / contact me check out my [github][1], [twitter][3], and my [personal site][2]. \n\n[1]: https://github.com/briwilcox\n[2]: http://brianmwilcox.com/\n[3]: https://twitter.com/brian_m_wilcox\n\n\nAuthors\n==============\n- Brian Wilcox",
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