Python Framework to analyse Git repositories
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PyDriller is a Python framework that helps developers in analyzing Git repositories. With PyDriller you can easily extract information such as commits, developers, modifications, diffs, and source codes.

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Table of contents


Very few! Just:

  • Python3
  • Git

The list of dependencies is shown in ./requirements.txt, however the installer takes care of installing them for you.


Installing PyDriller is easily done using pip. Assuming it is installed, just run the following from the command-line:

pip install pydriller

This will also install the necessary dependencies.


If you like to clone from source, you can do it with very simple steps.


It is suggested to make use of virtualenv:

> virtualenv -p python3 venv
> source venv/bin/activate


Clone the repo:

> git clone

and install the requirements:

> cd pydriller
> pip install -r requirements.txt

to run the tests (using pytest):

> unzip
> pip install -r test-requirements.txt
> pytest


For information on how to use PyDriller, refer to the official documentation:

or have a look at our example folder.

How to cite PyDriller

	title = "PyDriller: Python Framework for Mining Software Repositories",
	abstract = "Software repositories contain historical and valuable information about the overall development of software systems. Mining software repositories (MSR) is nowadays considered one of the most interesting growing fields within software engineering. MSR focuses on extracting and analyzing data available in software repositories to uncover interesting, useful, and actionable information about the system. Even though MSR plays an important role in software engineering research, few tools have been created and made public to support developers in extracting information from Git repository. In this paper, we present PyDriller, a Python Framework that eases the process of mining Git. We compare our tool against the state-of-the-art Python Framework GitPython, demonstrating that PyDriller can achieve the same results with, on average, 50% less LOC and significantly lower complexity.URL:",
	author = "Spadini, Davide and Aniche, Maurício and Bacchelli, Alberto",
	year = "2018",
	doi = "10.1145/3236024.3264598",
	booktitle = "The 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)",


This software is licensed under the Apache 2.0 License.