Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other.
Project description
DeepDiff v 6.3.1
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 3.7+ and PyPy3.
What is new?
Please check the ChangeLog file for the detailed information.
DeepDiff 6-3-1
This release includes many bug fixes.
- Bugfix deephash for paths by maggelus
- Bugfix deephash compiled regex maggelus
- Fix tests dependent on toml by martin-kokos
- Bugfix for
include_pathsfor nested dictionaries by kor4ik - Use tomli and tomli-w for dealing with tomli files by martin-kokos
- Bugfix for
datetime.dateby Alex Sauer-Budge
DeepDiff 6-3-0
PrefixOrSuffixOperator: This operator will skip strings that are suffix or prefix of each other.include_obj_callbackandinclude_obj_callback_strictare added by Håvard Thom.- Fixed a corner case where numpy's
np.float32nans are not ignored when usingignore_nan_equalityby Noam Gottlieb orjsonbecomes optional again.- Fix for
ignore_type_in_groupswith numeric values so it does not report number changes when the number types are different.
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 rubust 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
:mega: 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! :dancers:
Contribute
- 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!
Citing
How to cite this library (APA style):
Dehpour, S. (2023). DeepDiff (Version 6.3.1) [Software]. Available from https://github.com/seperman/deepdiff.
How to cite this library (Chicago style):
Dehpour, Sep. 2023. DeepDiff (version 6.3.1).
Authors
Please take a look at the AUTHORS file.
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