Built-in Continuous Color Scales in Python

Using Built-In Continuous Color Scales¶

Many Plotly Express functions accept a color_continuous_scale argument and many trace types have a colorscale attribute in their schema. Plotly comes with a large number of built-in continuous color scales, which can be referred to in Python code when setting the above arguments, either by name in a case-insensitive string e.g. px.scatter(color_continuous_scale="Viridis") or by reference e.g. go.Scatter(marker_colorscale=plotly.colors.sequential.Viridis). They can also be reversed by adding _r at the end e.g. "Viridis_r" or plotly.colors.sequential.Viridis_r.

The plotly.colours module is also available under plotly.express.colors so you can refer to it as px.colors.

When using continuous color scales, you will often want to configure various aspects of its range and colorbar.

Discrete Color Sequences¶

Plotly also comes with some built-in discrete color sequences which are not intended to be used with the color_continuous_scale argument as they are not designed for interpolation to occur between adjacent colors.

Named Built-In Continuous Color Scales¶

You can use any of the following names as string values to set continuous_color_scale or colorscale arguments. These strings are case-insensitive and you can append _r to them to reverse the order of the scale.

Built-in color scales are stored as lists of CSS colors:

Continuous Color Scales in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Built-In Sequential Color scales¶

A collection of predefined sequential colorscales is provided in the plotly.colors.sequential module. Sequential color scales are appropriate for most continuous data, but in some cases it can be helpful to use a diverging or cyclical color scale (see below).

Here are all the built-in scales in the plotly.colors.sequential module:

Note: RdBu was included in the sequential module by mistake, even though it is a diverging color scale. It is intentionally left in for backwards-compatibility reasons.

Built-In Diverging Color scales¶

A collection of predefined diverging color scales is provided in the plotly.colors.diverging module. Diverging color scales are appropriate for continuous data that has a natural midpoint other otherwise informative special value, such as 0 altitude, or the boiling point of a liquid. These scales are intended to be used when explicitly setting the midpoint of the scale.

Here are all the built-in scales in the plotly.colors.diverging module:

Built-In Cyclical Color scales¶

A collection of predefined cyclical color scales is provided in the plotly.colors.cyclical module. Cyclical color scales are appropriate for continuous data that has a natural cyclical structure, such as temporal data (hour of day, day of week, day of year, seasons) or complex numbers or other phase or angular data.

Here are all the built-in scales in the plotly.colors.cyclical module:

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( ... )

from dash import Dash, dcc, html

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

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