A Jupyter Widget for Interactive Data Visualization. Bifrost provides useful chart recommendations and easy integration with Pandas DataFrames. It also provides a variety of analysis tools:

• Chart history log for keeping track of your data analysis.
• Targeted graph suggestions to drive further data exploration.
• Interactive filters for quantitative and categorical fields.
• Aggregations and binning for axis encodings.
• An expressive Python API

The extension allows data scientists to build familiarity with a dataset without sacrificing the reproducibility of code. Changes made in the Bifrost GUI are automatically translated into Pandas Queries, allowing developers to jump back into scripting whenever it is most convenient.

You can install using pip:

pip install jupyter_bifrost

If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable the nbextension:

jupyter nbextension enable --py [--sys-prefix|--user|--system] jupyter_bifrost

Jupyter Bifrost is intended to be used in Jupyter Notebooks in JupyterLab. Start by importing the package:

from jupyter_bifrost import Chart

Then instantiate the chart object with a dataset:

chart = Chart("<my-dataset>.csv") #or df = pd.DataFrame() chart = Chart(df)

Finally, plot the dataset to open up the Bifrost GUI:

res = chart.plot() # the `res` DataFrame will always stay up to date with the filters and aggregations applied in the GUI

For additional help with the extension, take a look at the wiki, or the help menu located in the menu bar of the Bifrost GUI.

Create a dev environment:

conda create -n jupyter_bifrost-dev -c conda-forge nodejs yarn python jupyterlab pandas conda activate jupyter_bifrost-dev

Install the python. This will also build the TS package:

pip install -e ".[test, examples]"

Build JupyterLab extension:

yarn run build 

For classic notebook, you need to run:

jupyter nbextension install --sys-prefix --symlink --overwrite --py jupyter_bifrost jupyter nbextension enable --sys-prefix --py jupyter_bifrost 

Note that the --symlink flag doesn't work on Windows, so you will here have to run the install command every time that you rebuild your extension. For certain installations you might also need another flag instead of --sys-prefix, but we won't cover the meaning of those flags here.

If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the widget.

# Watch the source directory in one terminal, automatically rebuilding when needed jlpm watch # Run JupyterLab in another terminal jupyter lab

After a change wait for the build to finish and then refresh your browser and the changes should take effect.

If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.