Basics¶
- 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}}
- Set verbose level to 2 includes new_path when the path has changed for a report between t1 and t2:
>>> t1 = [1, 3] >>> t2 = [3, 2] >>> >>> >>> diff = DeepDiff(t1, t2, ignore_order=True, verbose_level=2) >>> pprint(diff) {'values_changed': {'root[0]': {'new_path': 'root[1]', 'new_value': 2, 'old_value': 1}}}
- 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 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}}}
Note
All the examples above use the default Text View. If you want traversing functionality in the results, use the Tree View. You just need to set view=’tree’ to get it in tree form.
Group By¶
group_by can be used when dealing with the list of dictionaries. It converts them from lists to a single dictionary with the key defined by group_by. The common use case is when reading data from a flat CSV, and the primary key is one of the columns in the CSV. We want to use the primary key instead of the CSV row number to group the rows. The group_by can do 2D group_by by passing a list of 2 keys.
- For example:
>>> [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody'}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Blue'}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple'}, ... ]
- Becomes:
>>> t1 = { ... 'AA': {'name': 'Joe', 'last_name': 'Nobody'}, ... 'BB': {'name': 'James', 'last_name': 'Blue'}, ... 'CC': {'name': 'Mike', 'last_name': 'Apple'}, ... }
- With that in mind, let’s take a look at the following:
>>> from deepdiff import DeepDiff >>> t1 = [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody'}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Blue'}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple'}, ... ] >>> >>> t2 = [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody'}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Brown'}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple'}, ... ] >>> >>> DeepDiff(t1, t2) {'values_changed': {"root[1]['last_name']": {'new_value': 'Brown', 'old_value': 'Blue'}}}
- Now we use group_by=’id’:
>>> DeepDiff(t1, t2, group_by='id') {'values_changed': {"root['BB']['last_name']": {'new_value': 'Brown', 'old_value': 'Blue'}}}
Note
group_by actually changes the structure of the t1 and t2. You can see this by using the tree view:
>>> diff = DeepDiff(t1, t2, group_by='id', view='tree')
>>> diff
{'values_changed': [<root['BB']['last_name'] t1:'Blue', t2:'Brown'>]}
>>> diff['values_changed'][0]
<root['BB']['last_name'] t1:'Blue', t2:'Brown'>
>>> diff['values_changed'][0].up
<root['BB'] t1:{'name': 'Ja...}, t2:{'name': 'Ja...}>
>>> diff['values_changed'][0].up.up
<root t1:{'AA': {'nam...}, t2:{'AA': {'nam...}>
>>> diff['values_changed'][0].up.up.t1
{'AA': {'name': 'Joe', 'last_name': 'Nobody'}, 'BB': {'name': 'James', 'last_name': 'Blue'}, 'CC': {'name': 'Mike', 'last_name': 'Apple'}}
- 2D Example:
>>> from pprint import pprint >>> from deepdiff import DeepDiff >>> >>> t1 = [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody'}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Blue'}, ... {'id': 'BB', 'name': 'Jimmy', 'last_name': 'Red'}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple'}, ... ] >>> >>> t2 = [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody'}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Brown'}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple'}, ... ] >>> >>> diff = DeepDiff(t1, t2, group_by=['id', 'name']) >>> pprint(diff) {'dictionary_item_removed': [root['BB']['Jimmy']], 'values_changed': {"root['BB']['James']['last_name']": {'new_value': 'Brown', 'old_value': 'Blue'}}}
Group By - Sort Key¶
group_by_sort_key is used to define how dictionaries are sorted if multiple ones fall under one group. When this parameter is used, group_by converts the lists of dictionaries into a dictionary of keys to lists of dictionaries. Then, group_by_sort_key is used to sort between the list.
For example, there are duplicate id values. If we only use group_by=’id’, one of the dictionaries with id of ‘BB’ will overwrite the other. However, if we also set group_by_sort_key=’name’, we keep both dictionaries with the id of ‘BB’.
- Example:
>>> [{'id': 'AA', 'int_id': 2, 'last_name': 'Nobody', 'name': 'Joe'}, ... {'id': 'BB', 'int_id': 20, 'last_name': 'Blue', 'name': 'James'}, ... {'id': 'BB', 'int_id': 3, 'last_name': 'Red', 'name': 'Jimmy'}, ... {'id': 'CC', 'int_id': 4, 'last_name': 'Apple', 'name': 'Mike'}]
- Becomes:
>>> {'AA': [{'int_id': 2, 'last_name': 'Nobody', 'name': 'Joe'}], ... 'BB': [{'int_id': 20, 'last_name': 'Blue', 'name': 'James'}, ... {'int_id': 3, 'last_name': 'Red', 'name': 'Jimmy'}], ... 'CC': [{'int_id': 4, 'last_name': 'Apple', 'name': 'Mike'}]}
- Example of using group_by_sort_key
>>> t1 = [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody', 'int_id': 2}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Blue', 'int_id': 20}, ... {'id': 'BB', 'name': 'Jimmy', 'last_name': 'Red', 'int_id': 3}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple', 'int_id': 4}, ... ] >>> >>> t2 = [ ... {'id': 'AA', 'name': 'Joe', 'last_name': 'Nobody', 'int_id': 2}, ... {'id': 'BB', 'name': 'James', 'last_name': 'Brown', 'int_id': 20}, ... {'id': 'CC', 'name': 'Mike', 'last_name': 'Apple', 'int_id': 4}, ... ] >>> >>> diff = DeepDiff(t1, t2, group_by='id', group_by_sort_key='name') >>> >>> pprint(diff) {'iterable_item_removed': {"root['BB'][1]": {'int_id': 3, 'last_name': 'Red', 'name': 'Jimmy'}}, 'values_changed': {"root['BB'][0]['last_name']": {'new_value': 'Brown', 'old_value': 'Blue'}}}
Back to DeepDiff 8.1.1 documentation!