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TsTables Examples

This document shows you a few examples of how to use TsTables to store and access data.

Basic Examples

Fetch the daily EURUSD exchange rate from FRED

This example fetches the daily EURUSD exchange rate from FRED, the St. Louis Fed's online database of economic data. TsTables isn't really designed for storing daily data, but this simple example illustrates how you can get a Pandas DataFrame and append it to a time series.

import tables
import tstables
import pandas.io.data as web
from datetime import *

# Create a class to describe the table structure. The column "timestamp" is required, and must be
# in the first position (pos=0) and have the type Int64.
class prices(tables.IsDescription):
    timestamp = tables.Int64Col(pos=0)
    price = tables.Float64Col(pos=1)

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

# This creates the time series, which is just a group called 'EURUSD' in the root of the HDF5 file.
ts = f.create_ts('/','EURUSD',prices)

start = datetime(2010,1,1)
end = datetime(2014,5,2)

euro = web.DataReader("DEXUSEU", "fred", start, end)
ts.append(euro)
f.flush() 

# Now, read in a month of data
read_start_dt = datetime(2014,1,1)
read_end_dt = datetime(2014,1,31)

jan = ts.read_range(read_start_dt,read_end_dt)

Load one month of minutely bitcoin price data

This example loads one month of minutely Bitcoin Price Index from CoinDesk. First, you'll need to download this CSV file. This example assumes that you've stored the CSV file in the current directory.

import tables
import tstables
import pandas
from datetime import *

# 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.