In addition to hist2d or hexbin as @askewchan suggested, you can use the same method that the accepted answer in the question you linked to uses. If you want to do that: import numpy as np import matplotlib.pyplot as plt from scipy.stats import gaussian_kde # Generate fake data x = np.random.normal(size=1000) y = x * … Read more
The important thing is to sort the dataframe by values where target is 0, 1, or 2. import numpy as np import pandas as pd from sklearn.datasets import load_iris import seaborn as sns iris = load_iris() iris = pd.DataFrame(data=np.c_[iris[‘data’], iris[‘target’]], columns=iris[‘feature_names’] + [‘target’]) # Sort the dataframe by target target_0 = iris.loc[iris[‘target’] == 0] target_1 … Read more
use lines for the second one: plot(density(MyData$Column1)) lines(density(MyData$Column2)) make sure the limits of the first plot are suitable, though.