DeepDiff 5.0.0 documentation!

DeepDiff Overview

The DeepDiff library includes the following modules:

  • DeepDiff For Deep Difference of 2 objects. DeepDiff

    It return the deep Difference of python objects. It can also be used to take the distance between objects. Deep Distance

  • DeepSearch Search for objects within other objects. DeepSearch

  • DeepHash Hash any object based on their content even if they are not “hashable” in Python’s eyes. DeepHash

  • Delta Delta of objects that can be applied to other objects. Imagine git commits but for structured data. Delta

  • Extract For extracting a path from an object Extract

Supported Python Versions

DeepDiff is rigorously tested against Python 3.5, 3.6, 3.7, 3.8 and Pypy3

NOTE: Python 2 is not supported any more. DeepDiff v3.3.0 was the last version to supprt Python 2.


Tutorials can be found on Zepworks blog


Install from PyPi:

pip install deepdiff

DeepDiff prefers to use Murmur3 for hashing. However you need to manually install Murmur3 by running:

pip install 'deepdiff[murmur]'

Otherwise DeepDiff will be using SHA256 for hashing which is a cryptographic hash and is considerably slower for hashing. However hashing is not usually the bottleneck when dealing with big objects. Read more about DeepDiff Optimizations

If you are running into trouble installing Murmur3, please take a look at the Troubleshoot section.


>>> from deepdiff import DeepDiff  # For Deep Difference of 2 objects
>>> from deepdiff import grep, DeepSearch  # For finding if item exists in an object
>>> from deepdiff import DeepHash  # For hashing objects based on their contents
>>> from deepdiff import Delta  # For creating delta of objects that can be applied later to other objects.
>>> from deepdiff import extract  # For extracting a path from an object

Supported data types

int, string, unicode, dictionary, list, tuple, set, frozenset, OrderedDict, NamedTuple, Numpy, custom objects and more!


Pycon 2016 Talk A talk was given about the basics of how DeepDiff does what it does at Pycon 2016. Diff it to Dig it Pycon 2016 video

You can find more information about the contents of that Pycon talk here:

Indices and tables