ConcurrentPandas 0.1.2

Description:

ConcurrentPandas 0.1.2

Concurrent-Pandas=================Concurrent Pandas-------------**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. Note: Concurrent Pandas is not associated with Quandl or Python Pandas, it just allows you to access them faster. ---####Features- **Working in Python 2 and 3**- **Sequential Downloading of Keys**- **Concurrent downloading of keys using thread or process pools**- **All Concurrent Downloading will automatically pick an optimal number of threads or processes to use for your system**- **Recursive data structure unpacking for key insertion** - Pass one or many: - Lists - Sets - Deques - 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.- **Automatic re-attempts if the download fails or times out** - Retries increase the time to try again with each successive failure- **Variety of data sources supported** - Quandl - Federal Reserve Economic Data - Google Finance - Yahoo Finance - More coming soon!- **Data is returned in a hashmap for fast lookups** ( *O(1) average case* ) - Hash Map Keys are the strings entered for lookup, buckets contain your Panda data frame---####Easy to use```# Define your keysyahoo_keys = ["aapl", "xom", "msft", "goog", "brk-b", "TSLA", "IRBT"]# Instantiate Concurrent Pandasfast_panda = concurrentpandas.ConcurrentPandas()# Set your data sourcefast_panda.set_source_yahoo_finance()# Insert your keysfast_panda.insert_keys(yahoo_keys)# Choose either asynchronous threads, processes, or a single sequential downloadfast_panda.consume_keys_asynchronous_threads()# The Concurrent Pandas object contains a dict of your results nowmymap = fast_panda.return_map()# Easily pull the data out of the map for your researchprint(mymap["aapl"].head)```---#####Installation InstructionsNote : only tested on LinuxTo install execute:```pip install ConcurrentPandas```---#####UpdatesNew in 0.1.2Ability to interact with stock optionsNow requires BeautifulSoup4, and Pandas 0.16 or newer.---#####MiscTested on Python 2.7.6 and Python 3.4.0 To see what else I'm building or follow / contact me check out my [github][1], [twitter][3], and my [personal site][2]. [1]: https://github.com/briwilcox[2]: http://brianmwilcox.com/[3]: https://twitter.com/brian_m_wilcoxAuthors==============- Brian Wilcox