DeepDiff v 8.5.0

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 3.9+ and PyPy3.

• Documentation

What is new?

Please check the ChangeLog file for the detailed information.

DeepDiff 8-5-0

• Updating deprecated pydantic calls
• Switching to pyproject.toml
• Fix for moving nested tables when using iterable_compare_func. by
• Fix recursion depth limit when hashing numpy.datetime64
• Moving from legacy setuptools use to pyproject.toml

DeepDiff 8-4-2

• fixes the type hints for the base
• fixes summarize so if json dumps fails, we can still get a repr of the results
• adds ipaddress support

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/

A message from Sep, the creator of DeepDiff

👋 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.

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!

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 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!

Authors

Please take a look at the AUTHORS file.