This is one of the 100 recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python.

4.11. Manipulating large heterogeneous tables with HDF5 and PyTables¶

In [ ]:
import numpy as np
import tables as tb


We create a new HDF5 file.

In [ ]:
f = tb.open_file('myfile.h5', 'w')


We will create a HDF5 table with two columns: the name of a city (a string with 64 characters at most), and its population (a 32 bit integer).

In [ ]:
dtype = np.dtype([('city', 'S64'), ('population', 'i4')])


Now, we create the table in '/table1'.

In [ ]:
table = f.create_table('/', 'table1', dtype)


In [ ]:
table.append([('Brussels', 1138854),
('London',   8308369),
('Paris',    2243833)])


After adding rows, we need to flush the table to commit the changes on disk.

In [ ]:
table.flush()


Data can be obtained from the table with a lot of different ways in PyTables. The easiest but less efficient way is to load the entire table in memory, which returns a NumPy array.

In [ ]:
table[:]


It is also possible to load a particular column (and all rows).

In [ ]:
table.col('city')


When dealing with a large number of rows, we can make a SQL-like query in the table to load all rows that satisfy particular conditions.

In [ ]:
[row['city'] for row in table.where('population>2e6')]


Finally, we can access particular rows knowing their indices.

In [ ]:
table[1]


Clean-up.

In [ ]:
f.close()
import os
os.remove('myfile.h5')


You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).

IPython Cookbook, by Cyrille Rossant, Packt Publishing, 2014 (500 pages).