nbsafety adds a layer of protection to computational notebooks by solving the stale dependency problem when executing cells out-of-order. Here's an example in action:

Step 0: modify cell 1 Step 1: rerun cell 1
example-0 example-1
Step 2: rerun cell 2 Step 3: rerun cell 3
example-2 example-3

When the first cell is rerun, the second cell now contains a reference to an updated f and is suggested for re-execution with a turquoise highlight. The third cell contains a reference to a stale y -- y is stale due to its dependency on an old value of f. As such, the third cell is marked as unsafe for re-execution with a red highlight. Once the second cell is rerun, it is now suggested to re-execute the third cell in order to refresh its stale output.

nbsafety accomplishes its magic using a combination of a runtime tracer (to build the implicit dependency graph) and a static checker (to provide warnings before running a cell), both of which are deeply aware of Python's data model. In particular, nbsafety requires minimal to no changes in user behavior, opting to get out of the way unless absolutely necessary and letting you use notebooks the way you prefer.


pip install nbsafety


The kernel ships with an extension that highlights cells with live references to stale symbols using red UI elements. It furthermore uses turquoise highlights for cells with live references to updated symbols, as well as for cells that resolve staleness.


To run an nbsafety kernel in Jupyter, select "Python 3 (nbsafety)" from the list of notebook types in Jupyter's "New" dropdown dialogue. For JupyterLab, similarly select "Python 3 (nbsafety)" from the list of available kernels in the Launcher tab.

Jupyter Notebook Entrypoint: Jupyter Lab Entrypoint:
nbsafety-notebook nbsafety-lab


pip uninstall nbsafety