Main feature
The primary feature of deepdish is its ability to save and load all kinds of data as HDF5. It can save any Python data structure, offering the same ease of use as pickling or numpy.save. However, it improves by also offering:
- Interoperability between languages (HDF5 is a popular standard)
- Easy to inspect the content from the command line (using h5ls or our specialized tool ddls)
- Highly compressed storage (thanks to a PyTables backend)
- Native support for scipy sparse matrices and pandas DataFrame, Series and Panel
- Ability to partially read files, even slices of arrays
An example:
import deepdish as dd d = { 'foo': np.ones((10, 20)), 'sub': { 'bar': 'a string', 'baz': 1.23, }, } dd.io.save('test.h5', d)
This can be reconstructed using dd.io.load('test.h5'), or inspected through the command line using either a standard tool:
$ h5ls test.h5
foo Dataset {10, 20}
sub Group
Or, better yet, our custom tool ddls (or python -m deepdish.io.ls):
$ ddls test.h5 /foo array (10, 20) [float64] /sub dict /sub/bar 'a string' (8) [unicode] /sub/baz 1.23 [float64]
Read more at Saving and loading data.
