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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 
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.
plotly.py is MIT Licensed. Plotly graphs can be viewed in Jupyter notebooks, standalone HTML files, or hosted online using Chart Studio Cloud.
Contact us for consulting, dashboard development, application integration, and feature additions.
plotly.py may be installed using pip...
pip install plotly==4.14.3
or conda.
conda install -c plotly plotly=4.14.3
For use in the Jupyter Notebook, install the notebook and ipywidgets
packages using pip:
pip install "notebook>=5.3" "ipywidgets>=7.5"
or conda:
conda install "notebook>=5.3" "ipywidgets>=7.5"
For use in JupyterLab, install the jupyterlab and ipywidgets
packages using pip:
pip install jupyterlab "ipywidgets>=7.5"
or conda:
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 jupyterlab-plotly@4.14.3
# OPTIONAL: Jupyter widgets extension for FigureWidget support
jupyter labextension install @jupyter-widgets/jupyterlab-manager plotlywidget@4.14.3
Please check out our Troubleshooting guide if you run into any problems with JupyterLab.
plotly.py supports static image export,
using either the kaleido
package (recommended, supported as of plotly version 4.9) or the orca
command line utility (legacy as of plotly version 4.9).
The kaleido package has no dependencies and can be installed
using pip...
$ pip install -U kaleido
or conda.
$ 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
Or, psutil can be installed using pip...
pip install psutil
and orca can be installed according to the instructions in the orca README.
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
separate plotly-geo package. This package can be installed using pip...
pip install plotly-geo==1.0.0
or conda
conda install -c plotly plotly-geo=1.0.0
The 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
or conda
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
Code and documentation copyright 2019 Plotly, Inc.
Code released under the MIT license.
Docs released under the Creative Commons license.
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