DeepDiff OLD 4.0.7 documentation!¶
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 even if they are not “hashable”.
DeepDiff works with Python 3.4, 3.5, 3.6, 3.7, Pypy3
NOTE: Python 2 is not supported any more. DeepDiff v3.3.0 was the last version to supprt Python 2.
Installation¶
Install from PyPi:
pip install deepdiff
DeepDiff prefers to use Murmur3 for hashing. However you have 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.
If you are running into trouble installing Murmur3, please take a look at the Troubleshoot
Importing¶
>>> 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
DeepDiff¶
Read The DeepDiff details in:
Short introduction
Supported data types¶
int, string, dictionary, list, tuple, set, frozenset, OrderedDict, NamedTuple and custom objects!
Ignore Order¶
Sometimes you don’t care about the order of objects when comparing them.
In those cases, you can set ignore_order=True
. However this flag
won’t report the repetitions to you. You need to additionally enable
report_repetition=True
for getting a report of repetitions.
List difference ignoring order or duplicates¶
>>> 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)
{}
Exclude types or paths¶
Exclude certain types from comparison¶
>>> 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}))
{}
Significant Digits¶
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')}}}
Serialization¶
- Serialize to json
>>> t1 = {1: 1, 2: 2, 3: 3}
>>> t2 = {1: 1, 2: "2", 3: 3}
>>> ddiff = DeepDiff(t1, t2)
>>> jsoned = ddiff.to_json()
>>> jsoned
'{"type_changes": {"root[2]": {"new_type": "str", "new_value": "2", "old_type": "int", "old_value": 2}}}'
And many more features! Read more in
Deep Search¶
Deep Search inside objects to find the item matching your criteria.
Note that is searches for either the path to match your criteria or the word in an item.
- Examples
Importing
>>> from deepdiff import DeepSearch, grep
>>> from pprint import pprint
DeepSearch comes with grep function which is easier to remember!
Search in list for string
>>> obj = ["long somewhere", "string", 0, "somewhere great!"]
>>> item = "somewhere"
>>> ds = obj | grep(item, verbose_level=2)
>>> print(ds)
{'matched_values': {'root[3]': 'somewhere great!', 'root[0]': 'long somewhere'}}
Search in nested data for string
>>> obj = ["something somewhere", {"long": "somewhere", "string": 2, 0: 0, "somewhere": "around"}]
>>> item = "somewhere"
>>> ds = obj | grep(item, verbose_level=2)
>>> pprint(ds, indent=2)
{ 'matched_paths': {"root[1]['somewhere']": 'around'},
'matched_values': { 'root[0]': 'something somewhere',
"root[1]['long']": 'somewhere'}}
Read more in the Deep Search references:
Deep Hash¶
DeepHash calculates the hash of objects based on their contents in a deterministic way. This way 2 objects with the same content should have the same hash.
The main usage of DeepHash is to calculate the hash of otherwise unhashable objects. For example you can use DeepHash to calculate the hash of a set or a dictionary!
The core of DeepHash is a deterministic serialization of your object into a string so it can be passed to a hash function. By default it uses Murmur 3 128 bit hash function. but you can pass another hash function to it if you want.
Read the details at:
Examples:
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)
{1: 2468916477072481777512283587789292749, 2: -35787773492556653776377555218122431491, ...}
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]
34150898645750099477987229399128149852
Read more in the Deep Hash reference:
Troubleshoot¶
Murmur3¶
Failed to build mmh3 when installing DeepDiff
DeepDiff prefers to use Murmur3 for hashing. However you have to manually install murmur3 by running: pip install mmh3
On MacOS Mojave some user experience difficulty when installing Murmur3.
The problem can be solved by running:
xcode-select –install
And then running
pip install mmh3
References¶
Indices and tables¶
Changelog¶
v4-0-7: Hashing of the number 1 vs. True
v4-0-6: found a tiny bug in Python formatting of numbers in scientific notation. Added a workaround.
v4-0-5: Fixing number diffing. Adding number_format_notation and number_to_string_func.
v4-0-4: Adding ignore_string_case and ignore_type_subclasses
v4-0-3: Adding versionbump tool for release
v4-0-2: Fixing installation issue where rst files are missing.
v4-0-1: Fixing installation Tarball missing requirements.txt . DeepDiff v4+ should not show up as pip installable for Py2. Making Murmur3 installation optional.
v4-0-0: Ending Python 2 support, Adding more functionalities and documentation for DeepHash. Switching to Pytest for testing. Switching to Murmur3 128bit for hashing. Fixing classes which inherit from classes with slots didn’t have all of their slots compared. Renaming ContentHash to DeepHash. Adding exclude by path and regex path to DeepHash. Adding ignore_type_in_groups. Adding match_string to DeepSearch. Adding Timedelta object diffing.
v3-5-0: Exclude regex path
v3-3-0: Searching for objects and class attributes
v3-2-2: Adding help(deepdiff)
v3-2-1: Fixing hash of None
v3-2-0: Adding grep for search: object | grep(item)
v3-1-3: Unicode vs. Bytes default fix
v3-1-2: NotPresent Fix when item is added or removed.
v3-1-1: Bug fix when item value is None (#58)
v3-1-0: Serialization to/from json
v3-0-0: Introducing Tree View
v2-5-3: Bug fix on logging for content hash.
v2-5-2: Bug fixes on content hash.
v2-5-0: Adding ContentHash module to fix ignore_order once and for all.
v2-1-0: Adding Deep Search. Now you can search for item in an object.
v2-0-0: Exclusion patterns better coverage. Updating docs.
v1-8-0: Exclusion patterns.
v1-7-0: Deep Set comparison.
v1-6-0: Unifying key names. i.e newvalue is new_value now. For backward compatibility, newvalue still works.
v1-5-0: Fixing ignore order containers with unordered items. Adding significant digits when comparing decimals. Changes property is deprecated.
v1-1-0: Changing Set, Dictionary and Object Attribute Add/Removal to be reported as Set instead of List. Adding Pypy compatibility.
v1-0-2: Checking for ImmutableMapping type instead of dict
v1-0-1: Better ignore order support
v1-0-0: Restructuring output to make it more useful. This is NOT backward compatible.
v0-6-1: Fixiing iterables with unhashable when order is ignored
v0-6-0: Adding unicode support
v0-5-9: Adding decimal support
v0-5-8: Adding ignore order for unhashables support
v0-5-7: Adding ignore order support
v0-5-6: Adding slots support
v0-5-5: Adding loop detection