DeepDiff v 9.0.0

DeepDiff is now part of Qluster.

If you're building workflows around data validation and correction, Qluster gives your team a structured way to manage rules, review failures, approve fixes, and reuse decisions—without building the entire system from scratch.

Modules

• 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 3.10+ and PyPy3.

• Documentation

What is new?

Please check the ChangeLog file for the detailed information.

DeepDiff 9-0-0

• migration note:
  • to_dict() and to_json() now accept a verbose_level parameter and always return a usable text-view dict. When the original view is 'tree', they default to verbose_level=2 for full detail. The old view_override parameter is removed. To get the previous results, you will need to pass the explicit verbose_level to to_json and to_dict if you are using the tree view.
• Dropping support for Python 3.9
• Support for python 3.14
• Added support for callable group_by thanks to @echan5
• Added FlatDeltaDict TypedDict for to_flat_dicts return type
• Fixed colored view display when all list items are removed thanks to @yannrouillard
• Fixed hasattr() swallowing AttributeError in __slots__ handling for objects with __getattr__ thanks to @tpvasconcelos
• Fixed ignore_order=True missing int-vs-float type changes
• Always use t1 path for reporting thanks to @devin13cox
• Fixed _convert_oversized_ints failing on NamedTuples
• Fixed orjson TypeError for integers exceeding 64-bit range
• Fixed parameter bug in to_flat_dicts where include_action_in_path and report_type_changes were not being passed through
• Fixed ignore_keys issue in detailed__dict__ thanks to @vitalis89
• Fixed logarithmic similarity type hint thanks to @ljames8
• Added Fraction numeric support thanks to @akshat62

Installation

Install from PyPi:

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 robust CSV parsing
• orjson for speed and memory optimized parsing
• pydantic

Documentation

https://zepworks.com/deepdiff/current/

ChangeLog

Please take a look at the CHANGELOG file.

Survey

📣 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! 👯

Local dev

1. Clone the repo

2. Switch to the dev branch

3. Create your own branch

4. Install dependencies

   • Method 1: Use uv to install the dependencies: uv sync --all-extras.
   • Method 2: Use pip: pip install -e ".[cli,coverage,dev,docs,static,test]"

5. Build uv build

Contribute

1. Please make your PR against the dev branch
2. 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 won't add the --runslow flag.

Or to see a more user friendly version, please run: pytest --cov=deepdiff --cov-report term-missing --runslow.

Thank you!

Authors

Please take a look at the AUTHORS file.