GitPython is a python library used to interact with git repositories, high-level like git-porcelain, or low-level like git-plumbing.
It provides abstractions of git objects for easy access of repository data, and additionally allows you to access the git repository more directly using either a pure python implementation, or the faster, but more resource intensive git command implementation.
The object database implementation is optimized for handling large quantities of objects and large datasets, which is achieved by using low-level structures and data streaming.
GitPython needs the git executable to be installed on the system and available
in your PATH for most operations.
If it is not in your PATH, you can help GitPython find it by setting
the GIT_PYTHON_GIT_EXECUTABLE=<path/to/git> environment variable.
- Git (1.7.x or newer)
- Python 2.7 to 3.5, while python 2.6 is supported on a best-effort basis.
The list of dependencies are listed in ./requirements.txt and ./test-requirements.txt.
The installer takes care of installing them for you.
If you have downloaded the source code:
python setup.py install
or if you want to obtain a copy from the Pypi repository:
pip install gitpython
Both commands will install the required package dependencies.
A distribution package can be obtained for manual installation at:
http://pypi.python.org/pypi/GitPython
If you like to clone from source, you can do it like so:
git clone https://github.com/gitpython-developers/GitPython
git submodule update --init --recursive
./init-tests-after-clone.shGitPython is not suited for long-running processes (like daemons) as it tends to
leak system resources. It was written in a time where destructors (as implemented
in the __del__ method) still ran deterministically.
In case you still want to use it in such a context, you will want to search the
codebase for __del__ implementations and call these yourself when you see fit.
Another way assure proper cleanup of resources is to factor out GitPython into a separate process which can be dropped periodically.
For Windows, we do regularly test it on Appveyor CI but not all test-cases pass - you may help improve them by exploring Issue #525.
Python 2.6 is supported on best-effort basis; which means that it is likely to deteriorate over time.
Important: Right after cloning this repository, please be sure to have executed
the ./init-tests-after-clone.sh script in the repository root. Otherwise
you will encounter test failures.
On Windows, make sure you have git-daemon in your PATH. For MINGW-git, the git-daemon.exe
exists in Git\mingw64\libexec\git-core\; CYGWIN has no daemon, but should get along fine
with MINGW's.
The easiest way to run tests is by using tox a wrapper around virtualenv. It will take care of setting up environments with the proper dependencies installed and execute test commands. To install it simply:
pip install tox
Then run:
tox
For more fine-grained control, you can use nose.
Please have a look at the contributions file.
- User Documentation
- Questions and Answers
- Please post on stackoverflow and use the
gitpythontag - Issue Tracker
- Post reproducible bugs and feature requests as a new issue.
Please be sure to provide the following information if posting bugs:
- GitPython version (e.g.
import git; git.__version__) - Python version (e.g.
python --version) - The encountered stack-trace, if applicable
- Enough information to allow reproducing the issue
- GitPython version (e.g.
- Post reproducible bugs and feature requests as a new issue.
Please be sure to provide the following information if posting bugs:
- Update/verify the version in the
VERSIONfile - Update/verify that the changelog has been updated
- Commit everything
- Run
git tag <version>to tag the version in Git - Run
make release - Finally, set the upcoming version in the
VERSIONfile, usually be incrementing the patch level, and possibly by appending-dev. Probably you want togit pushonce more.
New BSD License. See the LICENSE file.
Now that there seems to be a massive user base, this should be motivation enough to let git-python return to a proper state, which means
- no open pull requests
- no open issues describing bugs