Install

NetworkX requires Python 3.6, 3.7, or 3.8. If you do not already have a Python environment configured on your computer, please see the instructions for installing the full scientific Python stack.

Note

If you are on Windows and want to install optional packages (e.g., scipy), then you will need to install a Python distribution such as Anaconda, Enthought Canopy, Python(x,y), WinPython, or Pyzo. If you use one of these Python distribution, please refer to their online documentation.

Below we assume you have the default Python environment already configured on your computer and you intend to install networkx inside of it. If you want to create and work with Python virtual environments, please follow instructions on venv and virtual environments.

First, make sure you have the latest version of pip (the Python package manager) installed. If you do not, refer to the Pip documentation and install pip first.

Install the released version

Install the current release of networkx with pip:

$ pip install networkx

To upgrade to a newer release use the --upgrade flag:

$ pip install --upgrade networkx

If you do not have permission to install software systemwide, you can install into your user directory using the --user flag:

$ pip install --user networkx

Alternatively, you can manually download networkx from GitHub or PyPI. To install one of these versions, unpack it and run the following from the top-level source directory using the Terminal:

$ pip install .

Install the development version

If you have Git installed on your system, it is also possible to install the development version of networkx.

Before installing the development version, you may need to uninstall the standard version of networkx using pip:

$ pip uninstall networkx

Then do:

$ git clone https://github.com/networkx/networkx.git
$ cd networkx
$ pip install -e .

The pip install -e . command allows you to follow the development branch as it changes by creating links in the right places and installing the command line scripts to the appropriate locations.

Then, if you want to update networkx at any time, in the same directory do:

$ git pull

Optional packages

Note

Some optional packages (e.g., scipy, gdal) may require compiling C or C++ code. If you have difficulty installing these packages with pip, please review the instructions for installing the full scientific Python stack.

The following optional packages provide additional functionality.

  • NumPy (>= 1.15.4) provides matrix representation of graphs and is used in some graph algorithms for high-performance matrix computations.

  • SciPy (>= 1.1.0) provides sparse matrix representation of graphs and many numerical scientific tools.

  • pandas (>= 0.23.3) provides a DataFrame, which is a tabular data structure with labeled axes.

  • Matplotlib (>= 3.0.2) provides flexible drawing of graphs.

  • PyGraphviz (>= 1.5) and pydot (>= 1.2.4) provide graph drawing and graph layout algorithms via GraphViz.

  • PyYAML provides YAML format reading and writing.

  • gdal provides shapefile format reading and writing.

  • lxml used for GraphML XML format.

To install networkx and all optional packages, do:

$ pip install networkx[all]

To explicitly install all optional packages, do:

$ pip install numpy scipy pandas matplotlib pygraphviz pydot pyyaml gdal

Or, install any optional package (e.g., numpy) individually:

$ pip install numpy

Testing

NetworkX uses the Python pytest testing package. You can learn more about pytest on their homepage.

Test a source distribution

You can test the complete package from the unpacked source directory with:

pytest networkx

Test an installed package

From a shell command prompt you can test the installed package with:

pytest --pyargs networkx

If you have a file-based (not a Python egg) installation you can test the installed package with:

>>> import networkx as nx
>>> nx.test()

or:

python -c "import networkx as nx; nx.test()"
test(verbosity=1, doctest=False)

Run NetworkX tests.

Parameters
  • verbosity (integer, optional) – Level of detail in test reports. Higher numbers provide more detail.

  • doctest (bool, optional) – True to run doctests in code modules