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        "author": "Andy Fiedler",
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        "description": "TsTables\n========\n\nTsTables is a Python package to store time series data in HDF5 files\nusing PyTables. It stores time series data into daily partitions and\nprovides functions to query for subsets of data across partitions.\n\nIts goals are to support a workflow where tons (gigabytes) of time\nseries data are appended periodically to a HDF5 file, and need to be\nread many times (quickly) for analytical models and research.\n\nExample\n-------\n\nThis example reads in minutely bitcoin price data and then fetches a\nrange of data. For the full example here, and other examples, see\n`EXAMPLES.md <EXAMPLES.md>`__.\n\n.. code:: python\n\n    # Class to use as the table description\n    class BpiValues(tables.IsDescription):\n        timestamp = tables.Int64Col(pos=0)\n        bpi = tables.Float64Col(pos=1)\n\n    # Use pandas to read in the CSV data\n    bpi = pandas.read_csv('bpi_2014_01.csv',index_col=0,names=['date','bpi'],parse_dates=True)\n\n    f = tables.open_file('bpi.h5','a')\n\n    # Create a new time series\n    ts = f.create_ts('/','BPI',BpiValues)\n\n    # Append the BPI data\n    ts.append(bpi)\n\n    # Read in some data\n    read_start_dt = datetime(2014,1,4,12,00)\n    read_end_dt = datetime(2014,1,4,14,30)\n\n    rows = ts.read_range(read_start_dt,read_end_dt)\n\n    # `rows` will be a pandas DataFrame with a DatetimeIndex.\n\nHere is how to open a pre-existing ``bpi.h5`` HDF5 file and get that\ntimeseries from it.\n\n.. code:: python\n\n    f = tables.open_file('bpi.h5','r')\n    ts = f.root.BPI._f_get_timeseries()\n\n    # Read in some data\n    read_start_dt = datetime(2014,1,4,12,00)\n    read_end_dt = datetime(2014,1,4,14,30)\n\n    rows = ts.read_range(read_start_dt,read_end_dt)\n\nRunning unit tests\n------------------\n\nYou can run the unit test suite from the command line at the root of the\nrepository:\n\n``python setup.py test``\n\nPreliminary benchmarks\n----------------------\n\nThe main goal of TsTables is to make it very fast to read subsets of\ndata, given a date range. TsTables currently includes a simple benchmark\nto track progress towards that goal. To run it, after installing the\npackage, you can run ``tstables_benchmark`` from the command line or you\ncan import the package in a Python console and run it directly.\n\n.. code:: python\n\n    import tstables\n    tstables.Benchmark.main()\n\nRunning the benchmark both prints results out to the screen and saves\nthem in ``benchmark.txt``.\n\nThe benchmark loads one year of random secondly data (just the timestamp\ncolumn and a 32-bit integer \"price\" column) into a file, and then it\nreads random one hour chunks of data.\n\nCurrently, here's some benchmarks of TsTables (from a MacBook Pro with a\nSSD):\n\n+---------------------------------------------------------------+-----------------+\n| Metric                                                        | Results         |\n+===============================================================+=================+\n| Append one month of data (2.67 million rows)                  | 0.711 seconds   |\n+---------------------------------------------------------------+-----------------+\n| Fetch one hour of data into memory                            | 0.305 seconds   |\n+---------------------------------------------------------------+-----------------+\n| File size (one year of data, 32 million rows, uncompressed)   | 391.6 MB        |\n+---------------------------------------------------------------+-----------------+\n\nHDF5 supports zlib and other compression algorithms, which can be\nenabled through PyTables to reduce the file size. Without compression,\nthe HDF5 file size is approximately 1.8% larger than the raw data in\nbinary form, a drastically lower overhead than CSV files.\n\nContributing\n------------\n\nIf you are interested in the project (to contribute or to hear about\nupdates), email Andy Fiedler at andy@andyfiedler.com or submit a pull\nrequest.",
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