DeepDiff OLD 4.0.7 documentation!

DeepDiff Reference

Note

These docs are OUTDATED.

Visit Zepworks.com for the current documentations.

class deepdiff.diff.DeepDiff(t1, t2, ignore_order=False, report_repetition=False, significant_digits=None, number_format_notation='f', exclude_paths=None, exclude_regex_paths=None, exclude_types=None, ignore_type_in_groups=None, ignore_string_type_changes=False, ignore_numeric_type_changes=False, ignore_type_subclasses=False, ignore_string_case=False, number_to_string_func=None, verbose_level=1, view='text', hasher=None, **kwargs)

DeepDiff

Note

DeepDiff documentations are now hosted on Zepworks.com

What you see here are the old documentations.

Deep Difference of dictionaries, iterables, strings and almost any other object. It will recursively look for all the changes.

DeepDiff 3.0 added the concept of views. There is a default “text” view and a “tree” view.

Parameters

t1A dictionary, list, string or any python object that has __dict__ or __slots__

This is the first item to be compared to the second item

t2dictionary, list, string or almost any python object that has __dict__ or __slots__

The second item is to be compared to the first one

ignore_orderBoolean, defalt=False

ignores orders for iterables Note that if you have iterables contatining any unhashable, ignoring order can be expensive. Normally ignore_order does not report duplicates and repetition changes. In order to report repetitions, set report_repetition=True in addition to ignore_order=True

report_repetitionBoolean, default=False

reports repetitions when set True ONLY when ignore_order is set True too. This works for iterables. This feature currently is experimental and is not production ready.

significant_digitsint >= 0, default=None

By default the significant_digits compares only that many digits AFTER the decimal point. However you can set override that by setting the number_format_notation=”e” which will make it mean the digits in scientific notation.

Important: This will affect ANY number comparison when it is set.

Note: If ignore_numeric_type_changes is set to True and you have left significant_digits to the default of None, it gets automatically set to 55. The reason is that normally when numbers from 2 different types are compared, instead of comparing the values, we only report the type change. However when ignore_numeric_type_changes=True, in order compare numbers from different types to each other, we need to convert them all into strings. The significant_digits will be used to make sure we accurately convert all the numbers into strings in order to report the changes between them.

Internally it uses “{:.Xf}”.format(Your Number) to compare numbers where X=significant_digits when the number_format_notation is left as the default of “f” meaning fixed point.

Note that “{:.3f}”.format(1.1135) = 1.113, but “{:.3f}”.format(1.11351) = 1.114

For Decimals, Python’s format rounds 2.5 to 2 and 3.5 to 4 (to the closest even number)

When you set the number_format_notation=”e”, we use “{:.Xe}”.format(Your Number) where X=significant_digits.

number_format_notationstring, default=”f”

number_format_notation is what defines the meaning of significant digits. The default value of “f” means the digits AFTER the decimal point. “f” stands for fixed point. The other option is “e” which stands for exponent notation or scientific notation.

number_to_string_funcfunction, default=None

This is an advanced feature to give the user the full control into overriding how numbers are converted to strings for comparison. The default function is defined in https://github.com/seperman/deepdiff/blob/master/deepdiff/helper.py and is called number_to_string. You can define your own function to do that.

verbose_level: int >= 0, default = 1

Higher verbose level shows you more details. For example verbose level 1 shows what dictionary item are added or removed. And verbose level 2 shows the value of the items that are added or removed too.

exclude_paths: list, default = None

List of paths to exclude from the report. If only one item, you can path it as a string.

exclude_regex_paths: list, default = None

List of string regex paths or compiled regex paths objects to exclude from the report. If only one item, you can pass it as a string or regex compiled object.

hasher: default = DeepHash.murmur3_128bit

Hash function to be used. If you don’t want Murmur3, you can use Python’s built-in hash function by passing hasher=hash. This is for advanced usage and normally you don’t need to modify it.

view: string, default = text

Starting the version 3 you can choosethe view into the deepdiff results. The default is the text view which has been the only view up until now. The new view is called the tree view which allows you to traverse through the tree of changed items.

exclude_types: list, default = None

List of object types to exclude from the report.

ignore_string_type_changes: Boolean, default = False

Whether to ignore string type changes or not. For example b”Hello” vs. “Hello” are considered the same if ignore_string_type_changes is set to True.

ignore_numeric_type_changes: Boolean, default = False

Whether to ignore numeric type changes or not. For example 10 vs. 10.0 are considered the same if ignore_numeric_type_changes is set to True.

ignore_type_in_groups: Tuple or List of Tuples, default = None

ignores types when t1 and t2 are both within the same type group.

ignore_type_subclasses: Boolean, default = False

ignore type (class) changes when dealing with the subclasses of classes that were marked to be ignored.

ignore_string_case: Boolean, default = False

Whether to be case-sensitive or not when comparing strings. By settings ignore_string_case=False, strings will be compared case-insensitively.

Returns

A DeepDiff object that has already calculated the difference of the 2 items.

Supported data types

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

Text View

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_added’] is a set of strings thus this is called the text view.

See also

The following examples are using the default text view. The Tree View is introduced in DeepDiff v3 and provides traversing capabilitie through your diffed data and more! Read more about the Tree View at the bottom of this page.

Importing

>>> from deepdiff import DeepDiff
>>> from pprint import pprint

Same object returns empty

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = t1
>>> print(DeepDiff(t1, t2))
{}

Type of an item has changed

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:"2", 3:3}
>>> pprint(DeepDiff(t1, t2), indent=2)
{ 'type_changes': { 'root[2]': { 'new_type': <class 'str'>,
                                 'new_value': '2',
                                 'old_type': <class 'int'>,
                                 'old_value': 2}}}

Value of an item has changed

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:4, 3:3}
>>> pprint(DeepDiff(t1, t2, verbose_level=0), indent=2)
{'values_changed': {'root[2]': {'new_value': 4, 'old_value': 2}}}

Item added and/or removed

>>> t1 = {1:1, 3:3, 4:4}
>>> t2 = {1:1, 3:3, 5:5, 6:6}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff)
{'dictionary_item_added': [root[5], root[6]],
 'dictionary_item_removed': [root[4]]}

Set verbose level to 2 in order to see the added or removed items with their values

>>> t1 = {1:1, 3:3, 4:4}
>>> t2 = {1:1, 3:3, 5:5, 6:6}
>>> ddiff = DeepDiff(t1, t2, verbose_level=2)
>>> pprint(ddiff, indent=2)
{ 'dictionary_item_added': {'root[5]': 5, 'root[6]': 6},
  'dictionary_item_removed': {'root[4]': 4}}

String difference

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world"}}
>>> t2 = {1:1, 2:4, 3:3, 4:{"a":"hello", "b":"world!"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'values_changed': { 'root[2]': {'new_value': 4, 'old_value': 2},
                      "root[4]['b']": { 'new_value': 'world!',
                                        'old_value': 'world'}}}

String difference 2

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world!\nGoodbye!\n1\n2\nEnd"}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world\n1\n2\nEnd"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'values_changed': { "root[4]['b']": { 'diff': '--- \n'
                                                '+++ \n'
                                                '@@ -1,5 +1,4 @@\n'
                                                '-world!\n'
                                                '-Goodbye!\n'
                                                '+world\n'
                                                ' 1\n'
                                                ' 2\n'
                                                ' End',
                                        'new_value': 'world\n1\n2\nEnd',
                                        'old_value': 'world!\n'
                                                     'Goodbye!\n'
                                                     '1\n'
                                                     '2\n'
                                                     'End'}}}

>>>
>>> print (ddiff['values_changed']["root[4]['b']"]["diff"])
--- 
+++ 
@@ -1,5 +1,4 @@
-world!
-Goodbye!
+world
 1
 2
 End

List difference

>>> 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)
>>> pprint (ddiff, indent = 2)
{'iterable_item_removed': {"root[4]['b'][2]": 3, "root[4]['b'][3]": 4}}

List difference 2:

>>> 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)
>>> pprint (ddiff, indent = 2)
{ 'iterable_item_added': {"root[4]['b'][3]": 3},
  'values_changed': { "root[4]['b'][1]": {'new_value': 3, 'old_value': 2},
                      "root[4]['b'][2]": {'new_value': 2, 'old_value': 3}}}

List difference ignoring order or duplicates: (with the same dictionaries as above)

>>> 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)
{}

List difference ignoring order but reporting repetitions:

>>> from deepdiff import DeepDiff
>>> from pprint import pprint
>>> t1 = [1, 3, 1, 4]
>>> t2 = [4, 4, 1]
>>> ddiff = DeepDiff(t1, t2, ignore_order=True, report_repetition=True)
>>> pprint(ddiff, indent=2)
{ 'iterable_item_removed': {'root[1]': 3},
  'repetition_change': { 'root[0]': { 'new_indexes': [2],
                                      'new_repeat': 1,
                                      'old_indexes': [0, 2],
                                      'old_repeat': 2,
                                      'value': 1},
                         'root[3]': { 'new_indexes': [0, 1],
                                      'new_repeat': 2,
                                      'old_indexes': [3],
                                      'old_repeat': 1,
                                      'value': 4}}}

List that contains dictionary:

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:1, 2:2}]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:3}]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'dictionary_item_removed': [root[4]['b'][2][2]],
  'values_changed': {"root[4]['b'][2][1]": {'new_value': 3, 'old_value': 1}}}

Sets:

>>> t1 = {1, 2, 8}
>>> t2 = {1, 2, 3, 5}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint(ddiff)
{'set_item_added': [root[3], root[5]], 'set_item_removed': [root[8]]}

Named Tuples:

>>> from collections import namedtuple
>>> Point = namedtuple('Point', ['x', 'y'])
>>> t1 = Point(x=11, y=22)
>>> t2 = Point(x=11, y=23)
>>> pprint (DeepDiff(t1, t2))
{'values_changed': {'root.y': {'new_value': 23, 'old_value': 22}}}

Custom objects:

>>> class ClassA(object):
...     a = 1
...     def __init__(self, b):
...         self.b = b
...
>>> t1 = ClassA(1)
>>> t2 = ClassA(2)
>>>
>>> pprint(DeepDiff(t1, t2))
{'values_changed': {'root.b': {'new_value': 2, 'old_value': 1}}}

Object attribute added:

>>> t2.c = "new attribute"
>>> pprint(DeepDiff(t1, t2))
{'attribute_added': [root.c],
 'values_changed': {'root.b': {'new_value': 2, 'old_value': 1}}}

Approximate decimals comparison (Significant digits after the point):

>>> t1 = Decimal('1.52')
>>> t2 = Decimal('1.57')
>>> DeepDiff(t1, t2, significant_digits=0)
{}
>>> DeepDiff(t1, t2, significant_digits=1)
{'values_changed': {'root': {'new_value': Decimal('1.57'), 'old_value': Decimal('1.52')}}}

Approximate float comparison (Significant digits after the point):

>>> t1 = [ 1.1129, 1.3359 ]
>>> t2 = [ 1.113, 1.3362 ]
>>> pprint(DeepDiff(t1, t2, significant_digits=3))
{}
>>> pprint(DeepDiff(t1, t2))
{'values_changed': {'root[0]': {'new_value': 1.113, 'old_value': 1.1129},
                    'root[1]': {'new_value': 1.3362, 'old_value': 1.3359}}}
>>> pprint(DeepDiff(1.23*10**20, 1.24*10**20, significant_digits=1))
{'values_changed': {'root': {'new_value': 1.24e+20, 'old_value': 1.23e+20}}}

Approximate number comparison (significant_digits after the decimal point in scientific notation)

>>> DeepDiff(1024, 1020, significant_digits=2, number_format_notation="f")  # default is "f"
{'values_changed': {'root': {'new_value': 1020, 'old_value': 1024}}}
>>> DeepDiff(1024, 1020, significant_digits=2, number_format_notation="e")
{}

Defining your own number_to_string_func

Lets say you want the numbers comparison happen only for numbers above 100 for some reason.

>>> from deepdiff import DeepDiff
>>> from deepdiff.helper import number_to_string
>>> def custom_number_to_string(number, *args, **kwargs):
...     number = 100 if number < 100 else number
...     return number_to_string(number, *args, **kwargs)
...
>>> t1 = [10, 12, 100000]
>>> t2 = [50, 63, 100021]
>>> DeepDiff(t1, t2, significant_digits=3, number_format_notation="e")
{'values_changed': {'root[0]': {'new_value': 50, 'old_value': 10}, 'root[1]': {'new_value': 63, 'old_value': 12}}}
>>> 
>>> DeepDiff(t1, t2, significant_digits=3, number_format_notation="e",
...          number_to_string_func=custom_number_to_string)
{}

Note

All the examples for the text view work for the tree view too. You just need to set view=’tree’ to get it in tree form.

Ignore Type Changes

Type change

>>> 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)
>>> pprint (ddiff, indent = 2)
{ 'type_changes': { "root[4]['b']": { 'new_type': <class 'str'>,
                                      'new_value': 'world\n\n\nEnd',
                                      'old_type': <class 'list'>,
                                      'old_value': [1, 2, 3]}}}

And if you don’t care about the value of items that have changed type, please set verbose level to 0

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:"2", 3:3}
>>> pprint(DeepDiff(t1, t2, verbose_level=0), indent=2)
{ 'type_changes': { 'root[2]': { 'new_type': <class 'str'>,
                                 'old_type': <class 'int'>}}}

Exclude types

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}))
{}

ignore_type_in_groups

Ignore type changes between members of groups of types. For example if you want to ignore type changes between float and decimals etc. Note that this is a more granular feature. Most of the times the shortcuts provided to you are enough. The shortcuts are ignore_string_type_changes which by default is False and ignore_numeric_type_changes which is by default False. You can read more about those shortcuts in this page. ignore_type_in_groups gives you more control compared to the shortcuts.

For example lets say you have specifically str and byte datatypes to be ignored for type changes. Then you have a couple of options:

1. Set ignore_string_type_changes=True.

2. Or set ignore_type_in_groups=[(str, bytes)]. Here you are saying if we detect one type to be str and the other one bytes, do not report them as type change. It is exactly as passing ignore_type_in_groups=[DeepDiff.strings] or ignore_type_in_groups=DeepDiff.strings .

Now what if you want also typeA and typeB to be ignored when comparing agains each other?

1. ignore_type_in_groups=[DeepDiff.strings, (typeA, typeB)]

2. or ignore_type_in_groups=[(str, bytes), (typeA, typeB)]

ignore_string_type_changes Default: False

>>> DeepDiff(b'hello', 'hello', ignore_string_type_changes=True)
{}
>>> DeepDiff(b'hello', 'hello')
{'type_changes': {'root': {'old_type': <class 'bytes'>, 'new_type': <class 'str'>, 'old_value': b'hello', 'new_value': 'hello'}}}

ignore_numeric_type_changes Default: False

Ignore Type Number - Dictionary that contains float and integer >>> from deepdiff import DeepDiff >>> from pprint import pprint >>> t1 = {1: 1, 2: 2.22} >>> t2 = {1: 1.0, 2: 2.22} >>> ddiff = DeepDiff(t1, t2) >>> pprint(ddiff, indent=2) { ‘type_changes’: { ‘root[1]’: { ‘new_type’: <class ‘float’>,

‘new_value’: 1.0, ‘old_type’: <class ‘int’>, ‘old_value’: 1}}}

>>> ddiff = DeepDiff(t1, t2, ignore_type_in_groups=DeepDiff.numbers)
>>> pprint(ddiff, indent=2)
{}

Ignore Type Number - List that contains float and integer

>>> 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=DeepDiff.numbers)
>>> pprint(ddiff, indent=2)
{}

You can pass a list of tuples or list of lists if you have various type groups. When t1 and t2 both fall under one of these type groups, the type change will be ignored. DeepDiff already comes with 2 groups: DeepDiff.strings and DeepDiff.numbers . If you want to pass both: >>> ignore_type_in_groups = [DeepDiff.strings, DeepDiff.numbers]

ignore_type_in_groups example with custom objects:

>>> class Burrito:
...     bread = 'flour'
...     def __init__(self):
...         self.spicy = True
...
>>>
>>> class Taco:
...     bread = 'flour'
...     def __init__(self):
...         self.spicy = True
...
>>>
>>> burrito = Burrito()
>>> taco = Taco()
>>>
>>> burritos = [burrito]
>>> tacos = [taco]
>>>
>>> DeepDiff(burritos, tacos, ignore_type_in_groups=[(Taco, Burrito)], ignore_order=True)
{}

ignore_type_subclasses

Use ignore_type_subclasses=True so when ignoring type (class), the subclasses of that class are ignored too.

>>> from deepdiff import DeepDiff
>>> class ClassA:
...     def __init__(self, x, y):
...         self.x = x
...         self.y = y
...
>>> class ClassB:
...     def __init__(self, x):
...         self.x = x
...
>>> class ClassC(ClassB):
...     pass
...
>>> obj_a = ClassA(1, 2)
>>> obj_c = ClassC(3)
>>>
>>> DeepDiff(obj_a, obj_c, ignore_type_in_groups=[(ClassA, ClassB)], ignore_type_subclasses=False)
{'type_changes': {'root': {'old_type': <class '__main__.ClassA'>, 'new_type': <class '__main__.ClassC'>, 'old_value': <__main__.ClassA object at 0x10076a2e8>, 'new_value': <__main__.ClassC object at 0x10082f630>}}}
>>>
>>> DeepDiff(obj_a, obj_c, ignore_type_in_groups=[(ClassA, ClassB)], ignore_type_subclasses=True)
{'values_changed': {'root.x': {'new_value': 3, 'old_value': 1}}, 'attribute_removed': [root.y]}

ignore_string_case

Whether to be case-sensitive or not when comparing strings. By settings ignore_string_case=False, strings will be compared case-insensitively.

>>> DeepDiff(t1='Hello', t2='heLLO')
{'values_changed': {'root': {'new_value': 'heLLO', 'old_value': 'Hello'}}}
>>> DeepDiff(t1='Hello', t2='heLLO', ignore_string_case=True)
{}

Tree View

Starting the version 3 You can chooe 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. This view is very useful when dealing with nested objects. Note that tree view always returns results in the form of Python sets.

You can traverse through the tree elements!

Note

The Tree view is just a different representation of the diffed data. Behind the scene, DeepDiff creates the tree view first and then converts it to textual representation for the text view.

+---------------------------------------------------------------+
|                                                               |
|    parent(t1)              parent node            parent(t2)  |
|      +                          ^                     +       |
+------|--------------------------|---------------------|-------+
       |                      |   | up                  |
       | Child                |   |                     | ChildRelationship
       | Relationship         |   |                     |
       |                 down |   |                     |
+------|----------------------|-------------------------|-------+
|      v                      v                         v       |
|    child(t1)              child node               child(t2)  |
|                                                               |
+---------------------------------------------------------------+

Up

Move up to the parent node

Down

Move down to the child node

Path()

Get the path to the current node

T1

The first item in the current node that is being diffed

T2

The second item in the current node that is being diffed

Additional

Additional information about the node i.e. repetition

Repetition

Shortcut to get the repetition report

The tree view allows you to have more than mere textual representaion of the diffed objects. It gives you the actual objects (t1, t2) throughout the tree of parents and children.

Examples Tree View

Note

The Tree View is introduced in DeepDiff 3. Set view=’tree’ in order to use this view.

Value of an item has changed (Tree View)

>>> 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,...}>

List difference (Tree View)

>>> 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()
"root[4]['b']"
>>> parent.t1
[1, 2, 3, 4]
>>> parent.t2
[1, 2]
>>> parent.up
<root[4] t1:{'a': 'hello...}, t2:{'a': 'hello...}>
>>> parent.up.up
<root t1:{1: 1, 2: 2,...}, t2:{1: 1, 2: 2,...}>
>>> parent.up.up.t1
{1: 1, 2: 2, 3: 3, 4: {'a': 'hello', 'b': [1, 2, 3, 4]}}
>>> parent.up.up.t1 == t1  # It is holding the original t1 that we passed to DeepDiff
True

List difference 2 (Tree View)

>>> 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, view='tree')
>>> pprint(ddiff, indent = 2)
{ 'iterable_item_added': [<root[4]['b'][3] t1:not present, t2:3>],
  'values_changed': [<root[4]['b'][1] t1:2, t2:3>, <root[4]['b'][2] t1:3, t2:2>]}
>>>
>>> # Note that iterable_item_added is a set with one item.
>>> # So in order to get that one item from it, we can do:
>>>
>>> (added,) = ddiff['iterable_item_added']
>>> added
<root[4]['b'][3] t1:not present, t2:3>
>>> added.up.up
<root[4] t1:{'a': 'hello...}, t2:{'a': 'hello...}>
>>> added.up.up.path()
'root[4]'
>>> added.up.up.down
<root[4]['b'] t1:[1, 2, 3], t2:[1, 3, 2, 3]>
>>>
>>> # going up twice and then down twice gives you the same node in the tree:
>>> added.up.up.down.down == added
True

List difference ignoring order but reporting repetitions (Tree View)

>>> t1 = [1, 3, 1, 4]
>>> t2 = [4, 4, 1]
>>> ddiff = DeepDiff(t1, t2, ignore_order=True, report_repetition=True, view='tree')
>>> pprint(ddiff, indent=2)
{ 'iterable_item_removed': [<root[1] t1:3, t2:not present>],
  'repetition_change': [<root[3] {'repetition': {'old_repeat': 1,...}>, <root[0] {'repetition': {'old_repeat': 2,...}>]}
>>>
>>> # repetition_change is a set with 2 items.
>>> # in order to get those 2 items, we can do the following.
>>> # or we can convert the set to list and get the list items.
>>> # or we can iterate through the set items
>>>
>>> (repeat1, repeat2) = ddiff['repetition_change']
>>> repeat1  # the default verbosity is set to 1.
<root[3] {'repetition': {'old_repeat': 1,...}>
>>> # The actual data regarding the repetitions can be found in the repetition attribute:
>>> repeat1.repetition
{'old_repeat': 1, 'new_repeat': 2, 'old_indexes': [3], 'new_indexes': [0, 1]}
>>>
>>> # If you change the verbosity, you will see less:
>>> ddiff = DeepDiff(t1, t2, ignore_order=True, report_repetition=True, view='tree', verbose_level=0)
>>> ddiff
{'repetition_change': [<root[3]>, <root[0]>], 'iterable_item_removed': [<root[1]>]}
>>> (repeat1, repeat2) = ddiff['repetition_change']
>>> repeat1
<root[0]>
>>>
>>> # But the verbosity level does not change the actual report object.
>>> # It only changes the textual representaion of the object. We get the actual object here:
>>> repeat1.repetition
{'old_repeat': 1, 'new_repeat': 2, 'old_indexes': [3], 'new_indexes': [0, 1]}
>>> repeat1.t1
4
>>> repeat1.t2
4
>>> repeat1.up
<root>

List that contains dictionary (Tree View)

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:1, 2:2}]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:3}]}}
>>> ddiff = DeepDiff(t1, t2, view='tree')
>>> pprint (ddiff, indent = 2)
{ 'dictionary_item_removed': [<root[4]['b'][2][2] t1:2, t2:not present>],
  'values_changed': [<root[4]['b'][2][1] t1:1, t2:3>]}

Sets (Tree View):

>>> t1 = {1, 2, 8}
>>> t2 = {1, 2, 3, 5}
>>> ddiff = DeepDiff(t1, t2, view='tree')
>>> print(ddiff)
{'set_item_removed': [<root: t1:8, t2:not present>], 'set_item_added': [<root: t1:not present, t2:3>, <root: t1:not present, t2:5>]}
>>> # grabbing one item from set_item_removed set which has one item only
>>> (item,) = ddiff['set_item_removed']
>>> item.up
<root t1:{8, 1, 2}, t2:{1, 2, 3, 5}>
>>> item.up.t1 == t1
True

Named Tuples (Tree View):

>>> from collections import namedtuple
>>> Point = namedtuple('Point', ['x', 'y'])
>>> t1 = Point(x=11, y=22)
>>> t2 = Point(x=11, y=23)
>>> print(DeepDiff(t1, t2, view='tree'))
{'values_changed': [<root.y t1:22, t2:23>]}

Custom objects (Tree View):

>>> class ClassA(object):
...     a = 1
...     def __init__(self, b):
...         self.b = b
...
>>> t1 = ClassA(1)
>>> t2 = ClassA(2)
>>>
>>> print(DeepDiff(t1, t2, view='tree'))
{'values_changed': [<root.b t1:1, t2:2>]}

Object attribute added (Tree View):

>>> t2.c = "new attribute"
>>> pprint(DeepDiff(t1, t2, view='tree'))
{'attribute_added': [<root.c t1:not present, t2:'new attribute'>],
 'values_changed': [<root.b t1:1, t2:2>]}

Approximate decimals comparison (Significant digits after the point) (Tree View):

>>> t1 = Decimal('1.52')
>>> t2 = Decimal('1.57')
>>> DeepDiff(t1, t2, significant_digits=0, view='tree')
{}
>>> ddiff = DeepDiff(t1, t2, significant_digits=1, view='tree')
>>> ddiff
{'values_changed': [<root t1:Decimal('1.52'), t2:Decimal('1.57')>]}
>>> (change1,) = ddiff['values_changed']
>>> change1
<root t1:Decimal('1.52'), t2:Decimal('1.57')>
>>> change1.t1
Decimal('1.52')
>>> change1.t2
Decimal('1.57')
>>> change1.path()
'root'

Approximate float comparison (Significant digits after the point) (Tree View):

>>> t1 = [ 1.1129, 1.3359 ]
>>> t2 = [ 1.113, 1.3362 ]
>>> ddiff = DeepDiff(t1, t2, significant_digits=3, view='tree')
>>> ddiff
{}
>>> ddiff = DeepDiff(t1, t2, view='tree')
>>> pprint(ddiff, indent=2)
{ 'values_changed': [<root[0] t1:1.1129, t2:1.113>, <root[1] t1:1.3359, t2:1.3362>]}
>>> ddiff = DeepDiff(1.23*10**20, 1.24*10**20, significant_digits=1, view='tree')
>>> ddiff
{'values_changed': [<root t1:1.23e+20, t2:1.24e+20>]}

Exclude paths

Exclude part of your object tree from comparison use exclude_paths and pass a set or list of paths to exclude, if only one item is being passed, then just put it there as a string. No need to pass it as a list then.

>>> 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']"))  # one item pass it as a string
{}
>>> print (DeepDiff(t1, t2, exclude_paths=["root['ingredients']", "root['ingredients2']"]))  # multiple items pass as a list or a set.
{}

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.

>>> import re
>>> 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]))
{}

example 2:

>>> t1 = {'a': [1, 2, [3, {'foo1': 'bar'}]]}
>>> t2 = {'a': [1, 2, [3, {'foo2': 'bar'}]]}
>>> DeepDiff(t1, t2, exclude_regex_paths="\['foo.'\]")  # since it is one item in exclude_regex_paths, you don't have to put it in a list or a set.
{}

Tip: DeepDiff is using re.search on the path. So if you want to force it to match from the beginning of the path, add ^ to the beginning of regex.

Note

All the examples for the text view work for the tree view too. You just need to set view=’tree’ to get it in tree form.

Serialization

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"}}}'

See also

Take a look at to_json() documentation in this page for more details.

If you want the original DeepDiff object to be serialized with all the bells and whistles, you can use the to_json_pickle() and to_json_pickle() in order to serialize and deserialize its results into json. Note that json_pickle is unsafe and json pickle dumps from untrusted sources should never be loaded.

Serialize and then deserialize back to deepdiff

>>> t1 = {1: 1, 2: 2, 3: 3}
>>> t2 = {1: 1, 2: "2", 3: 3}
>>> ddiff = DeepDiff(t1, t2)
>>> jsoned = ddiff.to_json_pickle()
>>> jsoned
'{"type_changes": {"root[2]": {"new_type": {"py/type": "builtins.str"}, "new_value": "2", "old_type": {"py/type": "builtins.int"}, "old_value": 2}}}'
>>> ddiff_new = DeepDiff.from_json_pickle(jsoned)
>>> ddiff == ddiff_new
True

Pycon 2016 Talk I gave a talk about how DeepDiff does what it does at Pycon 2016. Diff it to Dig it Pycon 2016 video

And here is more info: http://zepworks.com/blog/diff-it-to-digg-it/

classmethod from_json_pickle(value)

Load DeepDiff object with all the bells and whistles from the json pickle dump. Note that json pickle dump comes from to_json_pickle

to_dict()

Dump dictionary of the text view. It does not matter which view you are currently in. It will give you the dictionary of the text view.

to_json(default_mapping=None)

Dump json of the text view. Parameters

default_mapping : default_mapping, dictionary(optional), a dictionary of mapping of different types to json types.

by default DeepDiff converts certain data types. For example Decimals into floats so they can be exported into json. If you have a certain object type that the json serializer can not serialize it, please pass the appropriate type conversion through this dictionary.

Example

Serialize custom objects

>>> class A:
...     pass
...
>>> class B:
...     pass
...
>>> t1 = A()
>>> t2 = B()
>>> ddiff = DeepDiff(t1, t2)
>>> ddiff.to_json()
TypeError: We do not know how to convert <__main__.A object at 0x10648> of type <class '__main__.A'> for json serialization. Please pass the default_mapping parameter with proper mapping of the object to a basic python type.

>>> default_mapping = {A: lambda x: 'obj A', B: lambda x: 'obj B'}
>>> ddiff.to_json(default_mapping=default_mapping)
'{"type_changes": {"root": {"old_type": "A", "new_type": "B", "old_value": "obj A", "new_value": "obj B"}}}'

to_json_pickle()

Get the json pickle of the diff object. Unless you need all the attributes and functionality of DeepDiff, running to_json() is the safer option that json pickle.

Back to DeepDiff OLD 4.0.7 documentation!