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Py.test plugin for validating Jupyter notebooks

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The plugin adds functionality to py.test to recognise and collect Jupyter notebooks. The intended purpose of the tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors.

The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently.

Each cell is taken as a test, a cell that doesn't reproduce the expected output will fail.

See docs/source/index.ipynb for the full documentation.


Available on PyPi:

pip install nbval

or install the latest version from cloning the repository and running:

pip install .

from the main directory. To uninstall:

pip uninstall nbval

How it works

The extension looks through every cell that contains code in an IPython notebook and then the py.test system compares the outputs stored in the notebook with the outputs of the cells when they are executed. Thus, the notebook itself is used as a testing function. The output lines when executing the notebook can be sanitized passing an extra option and file, when calling the py.test command. This file is a usual configuration file for the ConfigParser library.

Regarding the execution, roughly, the script initiates an IPython Kernel with a shell and an iopub sockets. The shell is needed to execute the cells in the notebook (it sends requests to the Kernel) and the iopub provides an interface to get the messages from the outputs. The contents of the messages obtained from the Kernel are organised in dictionaries with different information, such as time stamps of executions, cell data types, cell types, the status of the Kernel, username, etc.

In general, the functionality of the IPython notebook system is quite complex, but a detailed explanation of the messages and how the system works, can be found here



To execute this plugin, you need to execute py.test with the nbval flag to differentiate the testing from the usual python files:

py.test --nbval

You can also specify --nbval-lax, which runs notebooks and checks for errors, but only compares the outputs of cells with a #NBVAL_CHECK_OUTPUT marker comment.

py.test --nbval-lax

The commands above will execute all the .ipynb files in the current folder. Alternatively, you can execute a specific notebook:

py.test --nbval my_notebook.ipynb

If the output lines are going to be sanitized, an extra flag, --sanitize-with together with the path to a confguration file with regex expressions, must be passed, i.e.

py.test --nbval my_notebook.ipynb --sanitize-with path/to/my_sanitize_file

where my_sanitize_file has the following structure.

regex: [a-z]*
replace: abcd

regex: [1-9]*
replace: 0000

regex: foo
replace: bar

The regex option contains the expression that is going to be matched in the outputs, and replace is the string that will replace the regex match. Currently, the section names do not have any meaning or influence in the testing system, it will take all the sections and replace the corresponding options.


To use notebooks to generate coverage for imported code, use the pytest-cov plugin. nbval should automatically detect the relevant options and configure itself with it.

Parallel execution

nbval is compatible with the pytest-xdist plugin for parallel running of tests. It does however require the use of the --dist loadscope flag to ensure that all cells of one notebook are run on the same kernel.


The py.test system help can be obtained with py.test -h, which will show all the flags that can be passed to the command, such as the verbose -v option. The IPython notebook plugin can be found under the general section.


This plugin was inspired by Andrea Zonca's py.test plugin for collecting unit tests in the IPython notebooks (https://github.com/zonca/pytest-ipynb).

The original prototype was based on the template in https://gist.github.com/timo/2621679 and the code of a testing system for notebooks https://gist.github.com/minrk/2620735 which we integrated and mixed with the py.test system.

We acknowledge financial support from

  • OpenDreamKit Horizon 2020 European Research Infrastructures project (#676541), http://opendreamkit.org

  • EPSRC’s Centre for Doctoral Training in Next Generation Computational Modelling, http://ngcm.soton.ac.uk (#EP/L015382/1) and EPSRC’s Doctoral Training Centre in Complex System Simulation ((EP/G03690X/1),

  • The Gordon and Betty Moore Foundation through Grant GBMF #4856,by the Alfred P. Sloan Foundation and by the Helmsley Trust.


2014 - 2017 David Cortes-Ortuno, Oliver Laslett, T. Kluyver, Vidar Fauske, Maximilian Albert, MinRK, Ondrej Hovorka, Hans Fangohr