Closed
Description
Describe the bug
With certain specific data, DeepDiff gives a wrong result (see below for more details).
The issue seems to be related to repeated elements in the input, because even just small changes from the sequences below won't trigger the bug.
To Reproduce
from pprint import pprint
from deepdiff import DeepDiff
l1 = "A B C D E F G D H".split()
l2 = "B C X D H Y Z".split()
pprint(DeepDiff(l1, l2))
Result:
{'iterable_item_removed': {'root[0]': 'A', 'root[4]': 'E'},
'values_changed': {'root[2]': {'new_value': 'X', 'old_value': 'D'},
'root[5]': {'new_value': 'Y', 'old_value': 'F'},
'root[6]': {'new_value': 'Z', 'old_value': 'G'}}}
Problems with this:
- indexes in the
values_changed
key are not consistent:root[2]
old value is notD
, unless you take it after removing, butroot[5]
old value isF
only if you take it before removing. - Applying the diff (with some "interpretation" of the indexes) does not transform l1 into l2. We obtain
B C X Y Z D H
instead ofB C X D H Y Z
Expected behavior
Indexes in the values_changed
section should be consistent, and applying the diff on l1 should produce l2
OS, DeepDiff version and Python version (please complete the following information):
- OS: Any
- Python Version: 3.10.7
- DeepDiff Version: 6.7.1
Additional context
Note that changes to l1 or l2 will most probably produce a correct result.
This was the smallest example I could find that was reproducing the error.