
        
        
            
                
                
                    import matplotlib.pyplot as plt

ys = [6,3,6,5,8,5,7]
xs = [2,3,5,4,4,4,6]
labels = [0,1,1,1,0,1,1]
scatter = plt.scatter(xs, ys, c=labels)
plt.legend(handles=scatter.legend_elements()[0], labels=[0,1])
plt.show()

This generates the following plot:

Basic example of plotting scatter plot with string categories

To plot a scatter plot with string (non-integer) categories, use the following code:


        
        
            
                
                
                    import matplotlib.pyplot as plt
import pandas as pd

labels = ['A','B','A','C']
classes = pd.Categorical(labels).codes # convert labels into array of integers
scatter = plt.scatter([5,2,3,3], [1,2,4,1], c=classes)
plt.legend(handles=scatter.legend_elements()[0], labels=labels)
plt.show()

This results in the following plot:

Here, we are first converting our string labels into numerical values using Pandas' Categorical(~) function:


        
        
            
                
                
                    classes = pd.Categorical(labels).codes
classes
                
            
            array([0, 1, 0, 2], dtype=int8)

Plotting scatter plot with categories using custom colors

To plot a scatter plot with categories, use ListedColormap:


        
        
            
                
                
                    import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd

labels = ['A','B','A','C']
classes = pd.Categorical(labels).codes
colours = ListedColormap(['g','blue','#EA131B'])
scatter = plt.scatter([5,2,3,3], [1,2,4,1], c=classes, cmap=colours)
plt.legend(handles=scatter.legend_elements()[0], labels=labels)
plt.show()

This produces the following plot:

Published by Isshin Inada

Edited by 0 others

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