In [52]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors.kde import KernelDensity
import pandas as pd


    

In [53]:

    

data = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/MASS/geyser.csv', index_col=False)


    

In [105]:

    

X = data['waiting']
X = np.array(X)
X = X[:, np.newaxis]
kde = KernelDensity(kernel='epanechnikov').fit(X)


    

In [106]:

    

X_sample = np.linspace(40,100)[:, np.newaxis]
log_dens = kde.score_samples(X_sample)
plt.fill(X_sample, np.exp(log_dens))


    

    
Out[106]:





[<matplotlib.patches.Polygon at 0x7fa8824b5810>]






    

In [ ]: