F.A.Q¶
Note
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Q: DeepDiff report is not precise when ignore_order=True¶
>>> from deepdiff import DeepDiff
>>> from pprint import pprint
>>> t1 = [
... {
... "key": "some/pathto/customers/foo/",
... "flags": 0,
... "value": ""
... },
... {
... "key": "some/pathto/customers/foo/account_number",
... "flags": 0,
... "value": "somevalue1"
... }
... ]
>>>
>>> t2 = [
... {
... "key": "some/pathto/customers/foo/account_number",
... "flags": 0,
... "value": "somevalue2"
... },
... {
... "key": "some/pathto/customers/foo/",
... "flags": 0,
... "value": "new"
... }
... ]
>>>
>>> pprint(DeepDiff(t1, t2))
{'values_changed': {"root[0]['key']": {'new_value': 'some/pathto/customers/foo/account_number',
'old_value': 'some/pathto/customers/foo/'},
"root[0]['value']": {'new_value': 'somevalue2',
'old_value': ''},
"root[1]['key']": {'new_value': 'some/pathto/customers/foo/',
'old_value': 'some/pathto/customers/foo/account_number'},
"root[1]['value']": {'new_value': 'new',
'old_value': 'somevalue1'}}}
Answer
This is explained in Cutoff Distance For Pairs and Cutoff Intersection For Pairs
Bump up these 2 parameters to 1 and you get what you want:
>>> pprint(DeepDiff(t1, t2, ignore_order=True, cutoff_distance_for_pairs=1, cutoff_intersection_for_pairs=1))
{'values_changed': {"root[0]['value']": {'new_value': 'new', 'old_value': ''},
"root[1]['value']": {'new_value': 'somevalue2',
'old_value': 'somevalue1'}}}
Q: The report of changes in a nested dictionary is too granular¶
Answer
>>> from deepdiff import DeepDiff
>>> t1 = {"veggie": "carrots"}
>>> t2 = {"meat": "carrots"}
>>>
>>> DeepDiff(t1, t2, threshold_to_diff_deeper=0)
{'dictionary_item_added': ["root['meat']"], 'dictionary_item_removed': ["root['veggie']"]}
>>> DeepDiff(t1, t2, threshold_to_diff_deeper=0.33)
{'values_changed': {'root': {'new_value': {'meat': 'carrots'}, 'old_value': {'veggie': 'carrots'}}}}
Q: TypeError: Object of type type is not JSON serializable¶
I’m trying to serialize the DeepDiff results into json and I’m getting the TypeError.
>>> diff=DeepDiff(1, "a")
>>> diff
{'type_changes': {'root': {'old_type': <class 'int'>, 'new_type': <class 'str'>, 'old_value': 1, 'new_value': 'a'}}}
>>> json.dumps(diff)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File ".../json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File ".../json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File ".../json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type type is not JSON serializable
Answer
In order to serialize DeepDiff results into json, use to_json()
>>> diff.to_json()
'{"type_changes": {"root": {"old_type": "int", "new_type": "str", "old_value": 1, "new_value": "a"}}}'
Q: How do I parse DeepDiff result paths?¶
Answer
Use parse_path:
>>> from deepdiff import parse_path
>>> parse_path("root[1][2]['age']")
[1, 2, 'age']
>>> parse_path("root[1][2]['age']", include_actions=True)
[{'element': 1, 'action': 'GET'}, {'element': 2, 'action': 'GET'}, {'element': 'age', 'action': 'GET'}]
>>>
>>> parse_path("root['joe'].age")
['joe', 'age']
>>> parse_path("root['joe'].age", include_actions=True)
[{'element': 'joe', 'action': 'GET'}, {'element': 'age', 'action': 'GETATTR'}]
Or use the tree view so you can use path(output_format=’list’):
>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3, 4]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2]}}
>>> ddiff = DeepDiff(t1, t2, view='tree')
>>> ddiff
{'iterable_item_removed': [<root[4]['b'][2] t1:3, t2:not present>, <root[4]['b'][3] t1:4, t2:not present>]}
>>> # Note that the iterable_item_removed is a set. In this case it has 2 items in it.
>>> # One way to get one item from the set is to convert it to a list
>>> # And then get the first item of the list:
>>> removed = list(ddiff['iterable_item_removed'])[0]
>>> removed
<root[4]['b'][2] t1:3, t2:not present>
>>>
>>> parent = removed.up
>>> parent
<root[4]['b'] t1:[1, 2, 3, 4], t2:[1, 2]>
>>> parent.path() # gives you the string representation of the path
"root[4]['b']"
>>> parent.path(output_format='list') # gives you the list of keys and attributes that make up the path
[4, 'b']
Q: Why my datetimes are reported in UTC?¶
Answer
DeepDiff converts all datetimes into UTC. If a datetime is timezone naive, we assume it is in UTC too. That is different than what Python does. Python assumes your timezone naive datetime is in your local timezone. However, you can override it to any other time zone such as your Default Time Zone.
>>> from deepdiff import DeepDiff
>>> from datetime import datetime, timezone
>>> d1 = datetime(2020, 8, 31, 13, 14, 1)
>>> d2 = datetime(2020, 8, 31, 13, 14, 1, tzinfo=timezone.utc)
>>> d1 == d2
False
>>> DeepDiff(d1, d2)
{}
>>> d3 = d2.astimezone(pytz.timezone('America/New_York'))
>>> DeepDiff(d1, d3)
{}
>>> d1 == d3
False
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