This documents the development version of NetworkX. Documentation for the current release can be found here.

Contributor Guide


This document assumes some familiarity with contributing to open source scientific Python projects using GitHub pull requests. If this does not describe you, you may first want to see the New Contributor FAQ.

Development Workflow

  1. If you are a first-time contributor:

    • Go to and click the “fork” button to create your own copy of the project.

    • Clone the project to your local computer:

      git clone
    • Navigate to the folder networkx and add the upstream repository:

      git remote add upstream
    • Now, you have remote repositories named:

      • upstream, which refers to the networkx repository

      • origin, which refers to your personal fork

    • Next, you need to set up your build environment. Here are instructions for two popular environment managers:

      • venv (pip based)

        # Create a virtualenv named ``networkx-dev`` that lives in the directory of
        # the same name
        python -m venv networkx-dev
        # Activate it
        source networkx-dev/bin/activate
        # Install main development and runtime dependencies of networkx
        pip install -r <(cat requirements/{default,developer,test}.txt)
        # (Optional) Install pygraphviz, pydot, and gdal packages
        # These packages require that you have your system properly configured
        # and what that involves differs on various systems.
        # pip install -r requirements/extra.txt
        # Build and install networkx from source
        pip install -e .
        # Test your installation
        PYTHONPATH=. pytest networkx
      • conda (Anaconda or Miniconda)

        # Create a conda environment named ``networkx-dev``
        conda create --name networkx-dev
        # Activate it
        conda activate networkx-dev
        # Install main development and runtime dependencies of networkx
        conda install -c conda-forge `for i in requirements/{default,developer,test}.txt; do echo -n " --file $i "; done`
        # (Optional) Install pygraphviz, pydot, and gdal packages
        # These packages require that you have your system properly configured
        # and what that involves differs on various systems.
        # pip install -r requirements/extra.txt
        # Install networkx from source
        pip install -e . --no-deps
        # Test your installation
        PYTHONPATH=. pytest networkx
    • Finally, we recommend you use a pre-commit hook, which runs black when you type git commit:

      pre-commit install
  2. Develop your contribution:

    • Pull the latest changes from upstream:

      git checkout main
      git pull upstream main
    • Create a branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name such as ‘bugfix-for-issue-1480’:

      git checkout -b bugfix-for-issue-1480
    • Commit locally as you progress (git add and git commit)

  3. Test your contribution:

    • Run the test suite locally (see Testing for details):

      PYTHONPATH=. pytest networkx
    • Running the tests locally before submitting a pull request helps catch problems early and reduces the load on the continuous integration system.

  4. Submit your contribution:

    • Push your changes back to your fork on GitHub:

      git push origin bugfix-for-issue-1480
    • Go to GitHub. The new branch will show up with a green Pull Request button—click it.

    • If you want, post on the mailing list to explain your changes or to ask for review.

  5. Review process:

    • Every Pull Request (PR) update triggers a set of continuous integration services that check that the code is up to standards and passes all our tests. These checks must pass before your PR can be merged. If one of the checks fails, you can find out why by clicking on the “failed” icon (red cross) and inspecting the build and test log.

    • Reviewers (the other developers and interested community members) will write inline and/or general comments on your PR to help you improve its implementation, documentation, and style. Every single developer working on the project has their code reviewed, and we’ve come to see it as friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don’t let the review discourage you from contributing: its only aim is to improve the quality of project, not to criticize (we are, after all, very grateful for the time you’re donating!).

    • To update your PR, make your changes on your local repository and commit. As soon as those changes are pushed up (to the same branch as before) the PR will update automatically.


    If the PR closes an issue, make sure that GitHub knows to automatically close the issue when the PR is merged. For example, if the PR closes issue number 1480, you could use the phrase “Fixes #1480” in the PR description or commit message.

  6. Document changes

    If your change introduces any API modifications, please update doc/release/release_dev.rst.

    To set up a function for deprecation:

    • Use a deprecation warning to warn users. For example:

      msg = "curly_hair is deprecated and will be removed in v3.0. Use sum() instead."
      warnings.warn(msg, DeprecationWarning)
    • Add a warning to networkx/

          "ignore", category=DeprecationWarning, message=<start of message>
    • Add a reminder to doc/developer/deprecations.rst for the team to remove the deprecated functionality in the future. For example:

      * In ``utils/`` remove ``generate_unique_node`` and related tests.
    • Add a note (and a link to the PR) to doc/release/release_dev.rst:

      [`#4281 <>`_]
      Deprecate ``read_yaml`` and ``write_yaml``.


    To reviewers: make sure the merge message has a brief description of the change(s) and if the PR closes an issue add, for example, “Closes #123” where 123 is the issue number.

Divergence from upstream main

If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, merge the main branch into yours:

git fetch upstream main
git merge upstream/main

If any conflicts occur, they need to be fixed before continuing. See which files are in conflict using:

git status

Which displays a message like:

Unmerged paths:
  (use "git add <file>..." to mark resolution)

  both modified:   file_with_conflict.txt

Inside the conflicted file, you’ll find sections like these:

<<<<<<< HEAD
The way the text looks in your branch
The way the text looks in the main branch
>>>>>>> main

Choose one version of the text that should be kept, and delete the rest:

The way the text looks in your branch

Now, add the fixed file:

git add file_with_conflict.txt

Once you’ve fixed all merge conflicts, do:

git commit


Advanced Git users may want to rebase instead of merge, but we squash and merge PRs either way.


  • All code should have tests.

  • All code should be documented, to the same standard as NumPy and SciPy.

  • All changes are reviewed. Ask on the mailing list if you get no response to your pull request.

  • Default dependencies are listed in requirements/default.txt and extra (i.e., optional) dependencies are listed in requirements/extra.txt. We don’t often add new default and extra dependencies. If you are considering adding code that has a dependency, you should first consider adding a gallery example. Typically, new proposed dependencies would first be added as extra dependencies. Extra dependencies should be easy to install on all platforms and widely-used. New default dependencies should be easy to install on all platforms, widely-used in the community, and have demonstrated potential for wide-spread use in NetworkX.

  • Use the following import conventions:

    import numpy as np
    import scipy as sp
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import pandas as pd
    import networkx as nx

    After importing sp` for scipy:

    import scipy as sp

    use the following imports:

    import scipy.linalg  # call as sp.linalg
    import scipy.sparse  # call as sp.sparse
    import scipy.sparse.linalg  # call as sp.sparse.linalg
    import scipy.stats  # call as sp.stats
    import scipy.optimize  # call as sp.optimize

    For example, many libraries have a linalg subpackage: nx.linalg, np.linalg, sp.linalg, sp.sparse.linalg. The above import pattern makes the origin of any particular instance of linalg explicit.

  • Use the decorator not_implemented_for in networkx/utils/ to designate that a function doesn’t accept ‘directed’, ‘undirected’, ‘multigraph’ or ‘graph’. The first argument of the decorated function should be the graph object to be checked.

    @nx.not_implemented_for('directed', 'multigraph')
    def function_not_for_MultiDiGraph(G, others):
        # function not for graphs that are directed *and* multigraph
    def function_only_for_Graph(G, others):
        # function not for directed graphs *or* for multigraphs


networkx has an extensive test suite that ensures correct execution on your system. The test suite has to pass before a pull request can be merged, and tests should be added to cover any modifications to the code base. We make use of the pytest testing framework, with tests located in the various networkx/submodule/tests folders.

To run all tests:

$ PYTHONPATH=. pytest networkx

Or the tests for a specific submodule:

$ PYTHONPATH=. pytest networkx/readwrite

Or tests from a specific file:

$ PYTHONPATH=. pytest networkx/readwrite/tests/

Or a single test within that file:

$ PYTHONPATH=. pytest networkx/readwrite/tests/

Use --doctest-modules to run doctests. For example, run all tests and all doctests using:

$ PYTHONPATH=. pytest --doctest-modules networkx

Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%.

To measure the test coverage, run:

$ PYTHONPATH=. pytest --cov=networkx networkx

This will print a report with one line for each file in networkx, detailing the test coverage:

Name                                             Stmts   Miss Branch BrPart  Cover
networkx/                                33      2      2      1    91%
networkx/algorithms/                    114      0      0      0   100%
networkx/algorithms/approximation/       12      0      0      0   100%
networkx/algorithms/approximation/         42      1     18      1    97%