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Built-in Continuous Color Scales in Python Plotly

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  • Last Updated : 28 Feb, 2022

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:


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