Storing large Numpy arrays on disk: Python Pickle vs. HDF5

Sun, 10 Jan 2010 23:00:20 +0000

In a previous post, I described how Python’s Pickle module is fast and convenient for storing all sorts of data on disk. More recently, I showed how to profile the memory usage of Python code. In recent weeks, I’ve uncovered a serious limitation in the Pickle module when storing large amounts of data: Pickle requires a large amount of memory to save a data structure to disk. Fortunately, there is an open standard called HDF, which defines a binary file format that is designed to efficiently store large scientific data sets. I will demonstrate both approaches, and profile them to see how much memory is required. I am writing the HDF file using the PyTables interface. Here’s the little test program I’ve been using: [code language=“Python”]#!/usr/bin/env python from numpy import array

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Storing large Numpy arrays on disk: Python Pickle vs. HDF5