- DeepDiff: Deep Difference of dictionaries, iterables, strings, and ANY other object.
- DeepSearch: Search for objects within other objects.
- DeepHash: Hash any object based on their content.
- Delta: Store the difference of objects and apply them to other objects.
- Extract: Extract an item from a nested Python object using its path.
- commandline: Use DeepDiff from commandline.
Tested on Python 3.8+ and PyPy3.
Please check the ChangeLog file for the detailed information.
DeepDiff 8-2-0
- Small optimizations so we don't load functions that are not needed
- Updated the minimum version of Orderly-set
- Normalize all datetimes into UTC. Assume timezone naive datetimes are UTC.
DeepDiff 8-1-0
- Removing deprecated lines from setup.py
- Added
prefix
option topretty()
- Fixes hashing of numpy boolean values.
- Fixes slots comparison when the attribute doesn't exist.
- Relaxing orderly-set reqs
- Added Python 3.13 support
- Only lower if clean_key is instance of str #504
- Fixes issue where the key deep_distance is not returned when both compared items are equal #510
- Fixes exclude_paths fails to work in certain cases
- exclude_paths fails to work #509
- Fixes to_json() method chokes on standard json.dumps() kwargs such as sort_keys
- to_dict() method chokes on standard json.dumps() kwargs #490
- Fixes accessing the affected_root_keys property on the diff object returned by DeepDiff fails when one of the dicts is empty
- Fixes accessing the affected_root_keys property on the diff object returned by DeepDiff fails when one of the dicts is empty #508
pip install deepdiff
If you want to use DeepDiff from commandline:
pip install "deepdiff[cli]"
If you want to improve the performance of DeepDiff with certain functionalities such as improved json serialization:
pip install "deepdiff[optimize]"
Install optional packages:
- yaml
- tomli (python 3.10 and older) and tomli-w for writing
- clevercsv for more rubust CSV parsing
- orjson for speed and memory optimized parsing
- pydantic
https://zepworks.com/deepdiff/current/
👋 Hi there,
Thank you for using DeepDiff! As an engineer, I understand the frustration of wrestling with unruly data in pipelines. That's why I developed a new tool - Qluster to empower non-engineers to control and resolve data issues at scale autonomously and stop bugging the engineers! 🛠️
If you are going through this pain now, I would love to give you early access to Qluster and get your feedback.
Please take a look at the CHANGELOG file.
📣 Please fill out our fast 5-question survey so that we can learn how & why you use DeepDiff, and what improvements we should make. Thank you! 👯
- Please make your PR against the dev branch
- Please make sure that your PR has tests. Since DeepDiff is used in many sensitive data driven projects, we strive to maintain around 100% test coverage on the code.
Please run pytest --cov=deepdiff --runslow
to see the coverage report. Note that the --runslow
flag will run some slow tests too. In most cases you only want to run the fast tests which so you wont add the --runslow
flag.
Or to see a more user friendly version, please run: pytest --cov=deepdiff --cov-report term-missing --runslow
.
Thank you!
Please take a look at the AUTHORS file.