This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Deep Learning experiments from University of Chicago.

Project Description

deepdish

Deep learning and data science tools from the University of Chicago.

Installation

pip install deepdish

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.

Release History

Release History

This version
History Node

0.3.4

History Node

0.3.3

History Node

0.3.2

History Node

0.3.1

History Node

0.3.0

History Node

0.2.0

History Node

0.1.8

History Node

0.1.7

History Node

0.1.6

History Node

0.1.5

History Node

0.1.4

History Node

0.1.3

History Node

0.1.2

History Node

0.1.1

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
deepdish-0.3.4-py2.py3-none-any.whl (36.2 kB) Copy SHA256 Checksum SHA256 py2.py3 Wheel Jul 29, 2016
deepdish-0.3.4.tar.gz (35.7 kB) Copy SHA256 Checksum SHA256 Source Jul 29, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting