Built-in Continuous Color Scales in Python

A reference for the built-in named continuous (sequential, diverging and cyclical) color scales in Plotly.

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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.

In [1]:

import plotly.express as px from textwrap import wrap named_colorscales = px.colors.named_colorscales() print("\n".join(wrap("".join('{:<12}'.format(c) for c in named_colorscales), 96)))

aggrnyl agsunset blackbody bluered blues blugrn bluyl brwnyl bugn bupu burg burgyl cividis darkmint electric emrld gnbu greens greys hot inferno jet magenta magma mint orrd oranges oryel peach pinkyl plasma plotly3 pubu pubugn purd purp purples purpor rainbow rdbu rdpu redor reds sunset sunsetdark teal tealgrn turbo viridis ylgn ylgnbu ylorbr ylorrd algae amp deep dense gray haline ice matter solar speed tempo thermal turbid armyrose brbg earth fall geyser prgn piyg picnic portland puor rdgy rdylbu rdylgn spectral tealrose temps tropic balance curl delta oxy edge hsv icefire phase twilight mrybm mygbm

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

In [2]:

import plotly.express as px print(px.colors.sequential.Plasma)

['#0d0887', '#46039f', '#7201a8', '#9c179e', '#bd3786', '#d8576b', '#ed7953', '#fb9f3a', '#fdca26', '#f0f921']

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 & publish apps like this with Dash Enterprise or Plotly Cloud.

Out[3]:

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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:

In [4]:

import plotly.express as px fig = px.colors.sequential.swatches_continuous() fig.show()

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:

In [5]:

import plotly.express as px fig = px.colors.diverging.swatches_continuous() fig.show()

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:

In [6]:

import plotly.express as px fig = px.colors.cyclical.swatches_cyclical() fig.show() fig = px.colors.cyclical.swatches_continuous() fig.show()

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