An open-source, interactive data visualization library for Python
Data Science Workspaces
Our recommended IDE for Plotly’s Python graphing library is Dash Enterprise’s Data Science Workspaces, which has both Jupyter notebook and Python code file support.
pip install plotly==4.14.3
Inside Jupyter notebook (installable with
pip install "notebook>=5.3" "ipywidgets>=7.5"):
import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Scatter(y=[2, 1, 4, 3])) fig.add_trace(go.Bar(y=[1, 4, 3, 2])) fig.update_layout(title = 'Hello Figure') fig.show()
See the Python documentation for more examples.
Read about what's new in plotly.py v4
plotly.py is an interactive, open-source, and browser-based graphing library for Python :sparkles:
Built on top of plotly.js,
plotly.py is a high-level, declarative charting library. plotly.js ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and more.
Contact us for consulting, dashboard development, application integration, and feature additions.
- Online Documentation
- Contributing to plotly
- Code of Conduct
- Version 4 Migration Guide
- New! Announcing Dash 1.0
- Community forum
plotly.py may be installed using pip...
pip install plotly==4.14.3
conda install -c plotly plotly=4.14.3
Jupyter Notebook Support
For use in the Jupyter Notebook, install the
pip install "notebook>=5.3" "ipywidgets>=7.5"
conda install "notebook>=5.3" "ipywidgets>=7.5"
For use in JupyterLab, install the
pip install jupyterlab "ipywidgets>=7.5"
conda install jupyterlab "ipywidgets>=7.5"
Then run the following commands to install the required JupyterLab extensions (note that this will require
node to be installed):
# Basic JupyterLab renderer support jupyter labextension install email@example.com # OPTIONAL: Jupyter widgets extension for FigureWidget support jupyter labextension install @jupyter-widgets/jupyterlab-manager firstname.lastname@example.org
Please check out our Troubleshooting guide if you run into any problems with JupyterLab.
Static Image Export
kaleido package has no dependencies and can be installed
$ pip install -U kaleido
$ conda install -c conda-forge python-kaleido
While Kaleido is now the recommended image export approach because it is easier to install
and more widely compatible, static image export
can also be supported
by the legacy orca command line utility and the
psutil Python package.
These dependencies can both be installed using conda:
conda install -c plotly plotly-orca==1.3.1 psutil
psutil can be installed using pip...
pip install psutil
and orca can be installed according to the instructions in the orca README.
Extended Geo Support
Some plotly.py features rely on fairly large geographic shape files. The county
choropleth figure factory is one such example. These shape files are distributed as a
plotly-geo package. This package can be installed using pip...
pip install plotly-geo==1.0.0
conda install -c plotly plotly-geo=1.0.0
Chart Studio support
chart-studio package can be used to upload plotly figures to Plotly's Chart
Studio Cloud or On-Prem service. This package can be installed using pip...
pip install chart-studio==1.1.0
conda install -c plotly chart-studio=1.1.0
If you're migrating from plotly.py v3 to v4, please check out the Version 4 migration guide
If you're migrating from plotly.py v2 to v3, please check out the Version 3 migration guide
Copyright and Licenses
Code and documentation copyright 2019 Plotly, Inc.
Code released under the MIT license.
Docs released under the Creative Commons license.
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