Plotly has built-in discrete and continuous color scales. This article is about discrete color scales. A color continuous scale input is accepted by several Plotly Express functions, and many trace types have a color scale property in their schema. Plotly has a wide variety of built-in continuous color scales that can be referenced in Python code when specifying the arguments, either by name or by reference.
Code to print out the names of the color scales:
Python3
#import packages import plotly.express as px plotly_colorscales = px.colors.named_colorscales() # printing color scales print(plotly_colorscales) |
Output:
[‘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’]
Code to view the built-in sequential color scales in plotly.colors.sequential module. Swatches_sequential() method is used to view the color scales. The method returns a plot of all sequential color scales:
Python3
# importing packages import plotly.express as px fig = px.colors.sequential.swatches_continuous() fig.show() |
Output:
Example 1:
A scatter plot is plotted where the color of the scatterplot depends on the column ‘Light’. ‘size’ values represent continuous color.
To view and download the CSV files used in the examples click here.
Python3
# import packages and libraries import pandas as pd from matplotlib import pyplot as plt import numpy as np import plotly.express as px # reading the dataset df = pd.read_csv('weather.csv', encoding='UTF-8') # plot a scatterplot fig = px.scatter(df, x="Temperature", y='Humidity', color='Light', title="Numeric 'size' values represents continuous color") fig.show() |
Output:
Example 2:
The same example is repeated again but in the px. scatter() method we include an extra parameter ‘color_continous_scale’, name of the colour scale is given as input. px.colors.sequential contain color scales. In the given example Rainbow is the name of the color scale.
Python3
# import packages and libraries import pandas as pd from matplotlib import pyplot as plt import numpy as np import plotly.express as px # reading the dataset df = pd.read_csv('weather.csv', encoding='UTF-8') # creating a scatterplot fig = px.scatter(df, x="Temperature", y='Humidity', color='Light', color_continuous_scale=px.colors.sequential.Rainbow) fig.show() |
Output:
Example 3:
We can also specify the name of the color scale by using the name of the continuous color scale as a string. ‘Viridis’ is the name of the color scale.
Python3
# import packages and libraries import pandas as pd from matplotlib import pyplot as plt import numpy as np import plotly.express as px # reading the dataset df = pd.read_csv('weather.csv', encoding='UTF-8') # creating a scatterplot fig = px.scatter(df, x="Temperature", y='Humidity', color='Light', color_continuous_scale='Viridis') fig.show() |
Output:
