- DeepDiff: Deep Difference of dictionaries, iterables, strings and other objects. It will recursively look for all the changes.
- DeepSearch: Search for objects within other objects.
- DeepHash: Hash any object based on their content.
Tested on Python 3.6+ and PyPy3.
NOTE: Python 2 is not supported any more. DeepDiff v3.3.0 was the last version to support Python 2
NOTE: The last version of DeepDiff to work on Python 3.5 was DeepDiff 5-0-2
Deepdiff 5.5.0 comes with regular expressions in the DeepSearch and grep modules:
>>> from deepdiff import grep
>>> from pprint import pprint
>>> obj = ["something here", {"long": "somewhere", "someone": 2, 0: 0, "somewhere": "around"}]
>>> ds = obj | grep("some.*", use_regexp=True)
{ 'matched_paths': ["root[1]['someone']", "root[1]['somewhere']"],
'matched_values': ['root[0]', "root[1]['long']"]}pip install deepdiff
If you want to use DeepDiff from commandline:
pip install "deepdiff[cli]"
>>> 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 contentsNote: if you want to use DeepDiff via commandline, make sure to run pip install "deepdiff[cli]". Then you can access the commands via:
- DeepDiff
$ deep diff --help
- Delta
$ deep patch --help
- grep
$ deep grep --help
- extract
$ deep extract --help
DeepDiff gets the difference of 2 objects.
- Please take a look at the DeepDiff docs
- The full documentation of all modules can be found on https://zepworks.com/deepdiff/5.5.0/
- Tutorials and posts about DeepDiff can be found on https://zepworks.com/tags/deepdiff/
Note: This is just a brief overview of what DeepDiff can do. Please visit https://zepworks.com/deepdiff/5.5.0/ for full documentation.
>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 3, 2, 3]}}
>>> ddiff = DeepDiff(t1, t2, ignore_order=True)
>>> print (ddiff)
{}This flag ONLY works when ignoring order is enabled.
t1 = [1, 3, 1, 4]
t2 = [4, 4, 1]
ddiff = DeepDiff(t1, t2, ignore_order=True, report_repetition=True)
print(ddiff)which will print you:
{'iterable_item_removed': {'root[1]': 3},
'repetition_change': {'root[0]': {'old_repeat': 2,
'old_indexes': [0, 2],
'new_indexes': [2],
'value': 1,
'new_repeat': 1},
'root[3]': {'old_repeat': 1,
'old_indexes': [3],
'new_indexes': [0, 1],
'value': 4,
'new_repeat': 2}}}>>> l1 = logging.getLogger("test")
>>> l2 = logging.getLogger("test2")
>>> t1 = {"log": l1, 2: 1337}
>>> t2 = {"log": l2, 2: 1337}
>>> print(DeepDiff(t1, t2, exclude_types={logging.Logger}))
{}>>> t1 = {"for life": "vegan", "ingredients": ["no meat", "no eggs", "no dairy"]}
>>> t2 = {"for life": "vegan", "ingredients": ["veggies", "tofu", "soy sauce"]}
>>> print (DeepDiff(t1, t2, exclude_paths={"root['ingredients']"}))
{}You can also exclude using regular expressions by using exclude_regex_paths and pass a set or list of path regexes to exclude. The items in the list could be raw regex strings or compiled regex objects.
>>> t1 = [{'a': 1, 'b': 2}, {'c': 4, 'b': 5}]
>>> t2 = [{'a': 1, 'b': 3}, {'c': 4, 'b': 5}]
>>> print(DeepDiff(t1, t2, exclude_regex_paths={r"root\[\d+\]\['b'\]"}))
{}
>>> exclude_path = re.compile(r"root\[\d+\]\['b'\]")
>>> print(DeepDiff(t1, t2, exclude_regex_paths=[exclude_path]))
{}Digits after the decimal point. Internally it uses "{:.Xf}".format(Your Number) to compare numbers where X=significant_digits
>>> t1 = Decimal('1.52')
>>> t2 = Decimal('1.57')
>>> DeepDiff(t1, t2, significant_digits=0)
{}
>>> DeepDiff(t1, t2, significant_digits=1)
{'values_changed': {'root': {'old_value': Decimal('1.52'), 'new_value': Decimal('1.57')}}}>>> from deepdiff import DeepDiff
>>> from pprint import pprint
>>> t1 = [1, 2, 3]
>>> t2 = [1.0, 2.0, 3.0]
>>> ddiff = DeepDiff(t1, t2)
>>> pprint(ddiff, indent=2)
{ 'type_changes': { 'root[0]': { 'new_type': <class 'float'>,
'new_value': 1.0,
'old_type': <class 'int'>,
'old_value': 1},
'root[1]': { 'new_type': <class 'float'>,
'new_value': 2.0,
'old_type': <class 'int'>,
'old_value': 2},
'root[2]': { 'new_type': <class 'float'>,
'new_value': 3.0,
'old_type': <class 'int'>,
'old_value': 3}}}
>>> ddiff = DeepDiff(t1, t2, ignore_type_in_groups=[(int, float)])
{}Starting with DeepDiff v 3, there are two different views into your diffed data: text view (original) and tree view (new).
Text view is the original and currently the default view of DeepDiff.
It is called text view because the results contain texts that represent the path to the data:
Example of using the text view.
>>> from deepdiff import DeepDiff
>>> t1 = {1:1, 3:3, 4:4}
>>> t2 = {1:1, 3:3, 5:5, 6:6}
>>> ddiff = DeepDiff(t1, t2)
>>> print(ddiff)
{'dictionary_item_added': {'root[5]', 'root[6]'}, 'dictionary_item_removed': {'root[4]'}}So for example ddiff['dictionary_item_removed'] is a set if strings thus this is called the text view.
The following examples are using the *default text view.*
The Tree View is introduced in DeepDiff v3
and provides traversing capabilities through your diffed data and more!
Read more about the Tree View at the [tree view section](#tree-view) of this page.
Starting the version v3 You can choose the view into the deepdiff results. The tree view provides you with tree objects that you can traverse through to find the parents of the objects that are diffed and the actual objects that are being diffed.
>>> from deepdiff import DeepDiff
>>> from pprint import pprint
>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:4, 3:3}
>>> ddiff_verbose0 = DeepDiff(t1, t2, verbose_level=0, view='tree')
>>> ddiff_verbose0
{'values_changed': {<root[2]>}}
>>>
>>> ddiff_verbose1 = DeepDiff(t1, t2, verbose_level=1, view='tree')
>>> ddiff_verbose1
{'values_changed': {<root[2] t1:2, t2:4>}}
>>> set_of_values_changed = ddiff_verbose1['values_changed']
>>> # since set_of_values_changed includes only one item in a set
>>> # in order to get that one item we can:
>>> (changed,) = set_of_values_changed
>>> changed # Another way to get this is to do: changed=list(set_of_values_changed)[0]
<root[2] t1:2, t2:4>
>>> changed.t1
2
>>> changed.t2
4
>>> # You can traverse through the tree, get to the parents!
>>> changed.up
<root t1:{1: 1, 2: 2,...}, t2:{1: 1, 2: 4,...}>In order to convert the DeepDiff object into a normal Python dictionary, use the to_dict() method. Note that to_dict will use the text view even if you did the diff in tree view.
Example:
>>> t1 = {1: 1, 2: 2, 3: 3, 4: {"a": "hello", "b": [1, 2, 3]}}
>>> t2 = {1: 1, 2: 2, 3: 3, 4: {"a": "hello", "b": "world\n\n\nEnd"}}
>>> ddiff = DeepDiff(t1, t2, view='tree')
>>> ddiff.to_dict()
{'type_changes': {"root[4]['b']": {'old_type': <class 'list'>, 'new_type': <class 'str'>, 'old_value': [1, 2, 3], 'new_value': 'world\n\n\nEnd'}}}In order to do safe json serialization, use the to_json() method.
Example:
>>> t1 = {1: 1, 2: 2, 3: 3, 4: {"a": "hello", "b": [1, 2, 3]}}
>>> t2 = {1: 1, 2: 2, 3: 3, 4: {"a": "hello", "b": "world\n\n\nEnd"}}
>>> ddiff = DeepDiff(t1, t2, view='tree')
>>> ddiff.to_json()
'{"type_changes": {"root[4][\'b\']": {"old_type": "list", "new_type": "str", "old_value": [1, 2, 3], "new_value": "world\\n\\n\\nEnd"}}}'
- Please take a look at the DeepDiff docs
- The full documentation can be found on https://zepworks.com/deepdiff/5.5.0/
DeepDiff comes with a utility to find the path to the item you are looking for. It is called DeepSearch and it has a similar interface to DeepDiff.
Let's say you have a huge nested object and want to see if any item with the word somewhere exists in it.
Just grep through your objects as you would in shell!
from deepdiff import grep
obj = {"long": "somewhere", "string": 2, 0: 0, "somewhere": "around"}
ds = obj | grep("somewhere")
print(ds)Which will print:
{'matched_paths': {"root['somewhere']"},
'matched_values': {"root['long']"}}And you can pass all the same kwargs as DeepSearch to grep too:
>>> obj | grep(item, verbose_level=2)
{'matched_paths': {"root['somewhere']": 'around'}, 'matched_values': {"root['long']": 'somewhere'}}
- Please take a look at the DeepSearch docs
- The full documentation can be found on https://zepworks.com/deepdiff/5.5.0/
(New in v4-0-0)
DeepHash is designed to give you hash of ANY python object based on its contents even if the object is not considered hashable! DeepHash is supposed to be deterministic in order to make sure 2 objects that contain the same data, produce the same hash.
- Please take a look at the DeepHash docs
- The full documentation can be found on https://zepworks.com/deepdiff/5.5.0/
Let's say you have a dictionary object.
>>> from deepdiff import DeepHash
>>>
>>> obj = {1: 2, 'a': 'b'}If you try to hash it:
>>> hash(obj)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'dict'But with DeepHash:
>>> from deepdiff import DeepHash
>>> obj = {1: 2, 'a': 'b'}
>>> DeepHash(obj)
{4355639248: 2468916477072481777512283587789292749, 4355639280: -35787773492556653776377555218122431491, 4358636128: -88390647972316138151822486391929534118, 4358009664: 8833996863197925870419376694314494743, 4357467952: 34150898645750099477987229399128149852}So what is exactly the hash of obj in this case? DeepHash is calculating the hash of the obj and any other object that obj contains. The output of DeepHash is a dictionary of object IDs to their hashes. In order to get the hash of obj itself, you need to use the object (or the id of object) to get its hash:
>>> hashes = DeepHash(obj)
>>> hashes[obj]
34150898645750099477987229399128149852Which you can write as:
>>> hashes = DeepHash(obj)[obj]At first it might seem weird why DeepHash(obj)[obj] but remember that DeepHash(obj) is a dictionary of hashes of all other objects that obj contains too.
- Please take a look at the DeepHash docs
- The full documentation can be found on https://zepworks.com/deepdiff/5.5.0/
result is the output of the function that is being tests.
expected is the expected output of the function.
self.assertEqual(DeepDiff(expected, result), {})or if you are using Pytest:
assert not DeepDiff(expected, result)In other words, assert that there is no diff between the expected and the result.
Unlike Json Patch which is designed only for Json objects, DeepDiff is designed specifically for almost all Python types. In addition to that, DeepDiff checks for type changes and attribute value changes that Json Patch does not cover since there are no such things in Json. Last but not least, DeepDiff gives you the exact path of the item(s) that were changed in Python syntax.
Example in Json Patch for replacing:
{ "op": "replace", "path": "/a/b/c", "value": 42 }
Example in DeepDiff for the same operation:
>>> item1 = {'a':{'b':{'c':'foo'}}}
>>> item2 = {'a':{'b':{'c':42}}}
>>> DeepDiff(item1, item2)
{'type_changes': {"root['a']['b']['c']": {'old_type': <type 'str'>, 'new_value': 42, 'old_value': 'foo', 'new_type': <type 'int'>}}}https://zepworks.com/deepdiff/current/
I was honored to give a talk about the basics of how DeepDiff does what it does at Pycon 2016. Please check out the video and let me know what you think:
Diff It To Dig It Video And here is more info: http://zepworks.com/blog/diff-it-to-digg-it/
Please take a look at the CHANGELOG file.
We use bump2version to bump and tag releases.
git checkout master && git pull
bumpversion {patch|minor|major}
git push && git push --tags- Please make your PR against the dev branch
- Please make sure that your PR has tests. Since DeepDiff is used in many sensitive data driven projects, we strive to maintain around 100% test coverage on the code.
Please run pytest --cov=deepdiff --runslow to see the coverage report. Note that the --runslow flag will run some slow tests too. In most cases you only want to run the fast tests which so you won't add the --runslow flag.
Or to see a more user friendly version, please run: pytest --cov=deepdiff --cov-report term-missing --runslow.
Thank you!
Please take a look at the AUTHORS file.