Metadata-Version: 1.0
Name: tstables
Version: 0.0.7
Summary: Handles large time series using PyTables and Pandas
Home-page: http://github.com/afiedler/tstables
Author: Andy Fiedler
Author-email: andy@andyfiedler.com
License: MIT
Description: 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.
        
        Pre-release software
        --------------------
        
        TsTables is currently under development and has yet to be used
        extensively in production. It is reaching the point where it is
        reasonably well-tested, so if you'd like to use it, feel free! If you
        are interested in the project (to contribute or to hear about updates),
        email Andy Fiedler at andy@andyfiedler.com.
        
Keywords: time series high frequency HDF5
Platform: UNKNOWN
