igraph is a library for creating and manipulating graphs. It is intended to be as powerful (ie. fast) as possible to enable the analysis of large graphs.
This repository contains the source code to the Python interface of igraph.
You can learn more about python-igraph on our website.
We aim to provide wheels on PyPI for most of the stock Python versions; typically the three most recent minor releases from Python 3.x. Therefore, running the following command should work without having to compile anything during installation:
$ pip install python-igraph
See details in Installing Python Modules.
If you need to compile python-igraph from source for some reason, you need to install some dependencies first:
$ sudo apt install build-essential python-dev libxml2 libxml2-dev zlib1g-dev bison flex
and then run
$ pip install python-igraph
This should compile the C core of igraph as well as the Python extension automatically.
The source code of the Python package includes the source code of the matching
igraph version that the Python interface should compile against. However, if
you want to link the Python interface to a custom installation of the C core
that has already been compiled and installed on your system, you can ask
setup.py to use the pre-compiled version. This option requires that your
custom installation of igraph is discoverable with pkg-config. First, check
whether pkg-config can tell you the required compiler and linker flags for
igraph:
$ pkg-config --cflags --libs igraph
If pkg-config responds with a set of compiler and linker flags and not an
error message, you are probably okay. You can then proceed with the
installation using pip:
$ pip install python-igraph --install-option="--use-pkg-config"
Alternatively, if you have already downloaded and extracted the source code
of igraph, you can run setup.py directly:
$ python setup.py build --use-pkg-config
This option is primarily intended for package maintainers in Linux distributions so they can ensure that the packaged Python interface links to the packaged igraph library instead of bringing its own copy.
It is also useful on macOS if you want to link to the igraph library installed from Homebrew.
If you have downloaded the source code from Github and not PyPI, chances are that you have the latest development version, which contains a matching version of the C core of igraph as a git submodule. Therefore, to install the bleeding edge version, you need to instruct git to check out the submodules first:
git submodule update --init
Then, running the setup script should work if you have a C compiler and the necessary build dependencies (see the previous section):
$ sudo python setup.py build
Unit tests can be executed from the project directory with tox or with the
built-in unittest module:
$ python -m unittest
Contributions to python-igraph are welcome!
If you want to add a feature, fix a bug, or suggest an improvement, open an issue on this repository and we'll try to answer. If you have a piece of code that you would like to see included in the main tree, open a PR on this repo.
To start developing python-igraph, follow the steps below (these are
for Linux, Windows users should change the system commands a little).
First, clone this repo (e.g. via https) and enter the folder:
git clone https://github.com/igraph/python-igraph.git
cd python-igraphSecond, check out the necessary git submodules:
git submodule update --initand install igraph in development mode so your changes in the Python source code are picked up automatically by Python:
python setup.py developNOTE: Building requires autotools, a C compiler, and a few more dependencies.
Changes that you make to the Python code do not need any extra action. However,
if you adjust the source code of the C extension, you need to rebuild it by running
python setup.py develop again. However, compilation of igraph's C core is
cached in vendor/build and vendor/install so subsequent builds are much
faster than the first one as the C core does not need to be recompiled.
This version of python-igraph is compatible with PyPy and
is regularly tested on PyPy with tox. However, the
PyPy version falls behind the CPython version in terms of performance; for
instance, running all the tests takes ~5 seconds on my machine with CPython and
~15 seconds with PyPy. This can probably be attributed to the need for
emulating CPython reference counting, and does not seem to be alleviated by the
JIT.
There are also some subtle differences between the CPython and PyPy versions:
-
Docstrings defined in the C source code are not visible from PyPy.
-
GraphBaseis hashable and iterable in PyPy but not in CPython. SinceGraphBaseis internal anyway, this is likely to stay this way.