tstables 0.0.15
Handles large time series using PyTables and Pandas
License: MIT
Keywords: time series high frequency HDF5

TsTables
========

TsTables is a Python package to store time series data in HDF5 files
using PyTables. It stores time series data into daily partitions and
provides functions to query for subsets of data across partitions.

Its goals are to support a workflow where tons (gigabytes) of time
series data are appended periodically to a HDF5 file, and need to be
read many times (quickly) for analytical models and research.

Example
-------

This example reads in minutely bitcoin price data and then fetches a
range of data. For the full example here, and other examples, see
`EXAMPLES.md <EXAMPLES.md>`__.

.. code:: python

    # Class to use as the table description
    class BpiValues(tables.IsDescription):
        timestamp = tables.Int64Col(pos=0)
        bpi = tables.Float64Col(pos=1)

    # Use pandas to read in the CSV data
    bpi = pandas.read_csv('bpi_2014_01.csv',index_col=0,names=['date','bpi'],parse_dates=True)

    f = tables.open_file('bpi.h5','a')

    # Create a new time series
    ts = f.create_ts('/','BPI',BpiValues)

    # Append the BPI data
    ts.append(bpi)

    # Read in some data
    read_start_dt = datetime(2014,1,4,12,00)
    read_end_dt = datetime(2014,1,4,14,30)

    rows = ts.read_range(read_start_dt,read_end_dt)

    # `rows` will be a pandas DataFrame with a DatetimeIndex.

Here is how to open a pre-existing ``bpi.h5`` HDF5 file and get that
timeseries from it.

.. code:: python

    f = tables.open_file('bpi.h5','r')
    ts = f.root.BPI._f_get_timeseries()

    # Read in some data
    read_start_dt = datetime(2014,1,4,12,00)
    read_end_dt = datetime(2014,1,4,14,30)

    rows = ts.read_range(read_start_dt,read_end_dt)

Running unit tests
------------------

You can run the unit test suite from the command line at the root of the
repository:

``python setup.py test``

Preliminary benchmarks
----------------------

The main goal of TsTables is to make it very fast to read subsets of
data, given a date range. TsTables currently includes a simple benchmark
to track progress towards that goal. To run it, after installing the
package, you can run ``tstables_benchmark`` from the command line or you
can import the package in a Python console and run it directly.

.. code:: python

    import tstables
    tstables.Benchmark.main()

Running the benchmark both prints results out to the screen and saves
them in ``benchmark.txt``.

The benchmark loads one year of random secondly data (just the timestamp
column and a 32-bit integer "price" column) into a file, and then it
reads random one hour chunks of data.

Currently, here's some benchmarks of TsTables (from a MacBook Pro with a
SSD):

+---------------------------------------------------------------+-----------------+
| Metric                                                        | Results         |
+===============================================================+=================+
| Append one month of data (2.67 million rows)                  | 0.711 seconds   |
+---------------------------------------------------------------+-----------------+
| Fetch one hour of data into memory                            | 0.305 seconds   |
+---------------------------------------------------------------+-----------------+
| File size (one year of data, 32 million rows, uncompressed)   | 391.6 MB        |
+---------------------------------------------------------------+-----------------+

HDF5 supports zlib and other compression algorithms, which can be
enabled through PyTables to reduce the file size. Without compression,
the HDF5 file size is approximately 1.8% larger than the raw data in
binary form, a drastically lower overhead than CSV files.

Contributing
------------

If you are interested in the project (to contribute or to hear about
updates), email Andy Fiedler at andy@andyfiedler.com or submit a pull
request.