The power of Chart.js with Python
You can install ipychart from your terminal using pip or conda:
# using pip
$ pip install ipychart
# using conda
$ conda install -c conda-forge ipychart
Create charts with Python in a very similar way to creating charts using Chart.js. The charts created are fully configurable, interactive and modular and are displayed directly in the output of the the cells of your jupyter notebook environment:
You can also create charts directly from a pandas dataframe. See the Pandas Interface section of the documentation for more details.
For a development installation:
$ git clone https://github.com/nicohlr/ipychart.git
$ cd ipychart
$ conda install jupyterlab -c conda-forge
$ cd ipychart/src
$ jlpm install
$ cd ..
$ pip install -e .
$ jupyter nbextension install --py --symlink --sys-prefix ipychart
$ jupyter nbextension enable --py --sys-prefix ipychart
Ipychart is available under the MIT license.
FAQs
A Jupyter - Chart.js bridge enabling interactive data visualization with Python.
We found that ipychart demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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