nbinteract is an old research project and is no longer actively maintained. Try https://github.com/voila-dashboards/voila instead.
nbinteract is a Python package that creates interactive webpages from Jupyter
notebooks. nbinteract also has built-in support for interactive plotting.
These interactions are driven by data, not callbacks, allowing authors to focus
on the logic of their programs.
nbinteract is most useful for:
- Data scientists that want to create simple interactive blog posts without having to know / work with Javascript.
- Instructors that want to include interactive examples in their textbooks.
- Students that want to publish data analysis that contains interactive demos.
Currently, nbinteract is in an alpha stage because of its quickly-changing
API.
Most plotting functions from other libraries (e.g. matplotlib) take data as
input. nbinteract's plotting functions take functions as input.
import numpy as np
import nbinteract as nbi
def normal(mean, sd):
'''Returns 1000 points drawn at random fron N(mean, sd)'''
return np.random.normal(mean, sd, 1000)
# Pass in the `normal` function and let user change mean and sd.
# Whenever the user interacts with the sliders, the `normal` function
# is called and the returned data are plotted.
nbi.hist(normal, mean=(0, 10), sd=(0, 2.0), options=options)
Simulations are easy to create using nbinteract. In this simulation, we roll
a die and plot the running average of the rolls. We can see that with more
rolls, the average gets closer to the expected value: 3.5.
rolls = np.random.choice([1, 2, 3, 4, 5, 6], size=300)
averages = np.cumsum(rolls) / np.arange(1, 301)
def x_vals(num_rolls):
return range(num_rolls)
# The function to generate y-values gets called with the
# x-values as its first argument.
def y_vals(xs):
return averages[:len(xs)]
nbi.line(x_vals, y_vals, num_rolls=(1, 300))
From a notebook cell:
# Run in a notebook cell to convert the notebook into a publishable HTML page:
#
# nbi.publish('my_binder_spec', 'my_notebook.ipynb')
#
# Replace my_binder_spec with a Binder spec in the format
# {username}/{repo}/{branch} (e.g. SamLau95/nbinteract-image/master).
#
# Replace my_notebook.ipynb with the name of the notebook file to convert.
#
# Example:
nbi.publish('SamLau95/nbinteract-image/master', 'homepage.ipynb')From the command line:
# Run on the command line to convert the notebook into a publishable HTML page.
#
# nbinteract my_binder_spec my_notebook.ipynb
#
# Replace my_binder_spec with a Binder spec in the format
# {username}/{repo}/{branch} (e.g. SamLau95/nbinteract-image/master).
#
# Replace my_notebook.ipynb with the name of the notebook file to convert.
#
# Example:
nbinteract SamLau95/nbinteract-image/master homepage.ipynbFor more information on publishing, see the tutorial which has a complete walkthrough on publishing a notebook to the web.
Using pip:
pip install nbinteract
# The next two lines can be skipped for notebook version 5.3 and above
jupyter nbextension enable --py --sys-prefix widgetsnbextension
jupyter nbextension enable --py --sys-prefix bqplotYou may now import the nbinteract package in Python code and use the
nbinteract CLI command to convert notebooks to HTML pages.
Here's a link to the tutorial and docs for this project.
If you are interested in developing this project locally, run the following:
git clone https://github.com/SamLau95/nbinteract
cd nbinteract
# Installs the nbconvert exporter
pip install -e .
# To export a notebook to interactive HTML format:
jupyter nbconvert --to interact notebooks/Test.ipynb
pip install -U ipywidgets
jupyter nbextension enable --py --sys-prefix widgetsnbextension
brew install yarn
yarn install
# Start notebook and webpack servers
make -j2 serve
If you have any questions or comments, send us a message on the Gitter channel. We appreciate your feedback!
nbinteract is originally developed by Sam Lau and Caleb Siu as part of
a Masters project at UC Berkeley. The code lives under a BSD 3 license and we
welcome contributions and pull requests from the community.
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