Black Lives Matter. Please consider donating to Black Girls Code today.

Getting Started with Plotly in Python

Getting Started with Plotly for Python.


If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.
Find out if your company is using Dash Enterprise.


New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Overview

The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.

Built on top of the Plotly JavaScript library (plotly.js), plotly enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash. The plotly Python library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library.

Thanks to deep integration with the orca image export utility, plotly also provides great support for non-web contexts including desktop editors (e.g. QtConsole, Spyder, PyCharm) and static document publishing (e.g. exporting notebooks to PDF with high-quality vector images).

This Getting Started guide explains how to install plotly and related optional pages. Once you've installed, you can use our documentation in three main ways:

  1. You jump right in to examples of how to make basic charts, statistical charts, scientific charts, financial charts, maps, and 3-dimensional charts.
  2. If you prefer to learn about the fundamentals of the library first, you can read about the structure of figures, how to create and update figures, how to display figures, how to theme figures with templates, how to export figures to various formats and about Plotly Express, the high-level API for doing all of the above.
  3. You can check out our exhaustive reference guides: the Python API reference or the Figure Reference

For information on using Python to build web applications containing plotly figures, see the Dash User Guide.

We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.

Installation

plotly may be installed using pip...

$ pip install plotly==4.13.0

or conda.

$ conda install -c plotly plotly=4.13.0

This package contains everything you need to write figures to standalone HTML files.

Note: No internet connection, account, or payment is required to use plotly.py. Prior to version 4, this library could operate in either an "online" or "offline" mode. The documentation tended to emphasize the online mode, where graphs get published to the Chart Studio web service. In version 4, all "online" functionality was removed from the plotly package and is now available as the separate, optional, chart-studio package (See below). plotly.py version 4 is "offline" only, and does not include any functionality for uploading figures or data to cloud services.

In [1]:
import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.write_html('first_figure.html', auto_open=True)

Jupyter Notebook Support

For use in the classic Jupyter Notebook, install the notebook and ipywidgets packages using pip...

$ pip install "notebook>=5.3" "ipywidgets>=7.2"

or conda.

$ conda install "notebook>=5.3" "ipywidgets>=7.2"

These packages contain everything you need to run a Jupyter notebook...

$ jupyter notebook

and display plotly figures inline using the notebook renderer...

In [2]:
import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.show()

or using FigureWidget objects.

In [3]:
import plotly.graph_objects as go
fig = go.FigureWidget(data=go.Bar(y=[2, 3, 1]))
fig

See Displaying Figures in Python for more information on the renderers framework, and see Plotly FigureWidget Overview for more information on using FigureWidget.

JupyterLab Support (Python 3.5+)

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):

# JupyterLab renderer support
jupyter labextension install jupyterlab-plotly@4.13.0

# OPTIONAL: Jupyter widgets extension
jupyter labextension install @jupyter-widgets/jupyterlab-manager plotlywidget@4.13.0

These packages contain everything you need to run JupyterLab...

$ jupyter lab

and display plotly figures inline using the plotly_mimetype renderer...

In [4]:
import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.show()

or using FigureWidget objects (if the "OPTIONAL" step above was executed).

In [5]:
import plotly.graph_objects as go
fig = go.FigureWidget(data=go.Bar(y=[2, 3, 1]))
fig

Please check out our Troubleshooting guide if you run into any problems with JupyterLab, particularly if you are using multiple python environments inside Jupyter.

See Displaying Figures in Python for more information on the renderers framework, and see Plotly FigureWidget Overview for more information on using FigureWidget.

Static Image Export

plotly.py supports static image export, using the 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).

Kaleido

The kaleido package has no dependencies and can be installed using pip...

$ pip install -U kaleido

or conda.

$ conda install -c plotly python-kaleido

Orca

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.

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 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

See USA County Choropleth Maps in Python for more information on the county choropleth figure factory.

Chart Studio Support

The chart-studio package can be used to upload plotly figures to Plotly's Chart Studio Cloud or On-Prem services. This package can be installed using pip...

$ pip install chart-studio==1.0.0

or conda.

$ conda install -c plotly chart-studio=1.0.0

Note: This package is optional, and if it is not installed it is not possible for figures to be uploaded to the Chart Studio cloud service.

Where to next?

Once you've installed, you can use our documentation in three main ways:

  1. You jump right in to examples of how to make basic charts, statistical charts, scientific charts, financial charts, maps, and 3-dimensional charts.
  2. If you prefer to learn about the fundamentals of the library first, you can read about the structure of figures, how to create and update figures, how to display figures, how to theme figures with templates, how to export figures to various formats and about Plotly Express, the high-level API for doing all of the above.
  3. You can check out our exhaustive reference guides: the Python API reference or the Figure Reference

For information on using Python to build web applications containing plotly figures, see the Dash User Guide.

We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter