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.