Drawing Kernel Density Estimation-KDE Plot Using Pandas DataFrame

Overview:

  • Kernel Density Estimation(KDE) is a non-parametric way to find the Probability Density Function(PDF) of a given data.
  • Kernel Density Estimation can be applied regardless of the underlying distribution of the dataset.
  • The Kernel Density Estimation function has a smoothing parameter or bandwidth ‘h’ based on which the resulting PDF is either a close-fit or an under-fit or an over-fit.

 

Drawing a Kernel Density Estimation-KDE plot using pandas DataFrame:

  • In Python, invoking the kde() method on the plot member of a pandas DataFrame class draws a Kernel Density Estimation plot.

Example:

# Python example program to plot Probability Density Function

# using Kernel Density Estimation(KDE)

import pandas as pd

import matplotlib.pyplot as plot

 

# Data as a Python Dictionary

dataDictionary = {"Lucas series":[2, 1, 3, 4, 7, 11, 18, 29, 47, 76],

"Hexagonal series":[1, 6, 15, 28, 45, 66, 91, 120, 153, 190]};

 

# Create a DataFrame

dataFrame = pd.DataFrame(data = dataDictionary);

 

# Plot PDF using KDE with different bandwidth values

dataFrame.plot.kde(title="PDF using Kernel Density Estimation - Bandwidth method:scott");

dataFrame.plot.kde(bw_method=0.3, title="PDF using Kernel Density Estimation - Bandwidth value=0.3");

dataFrame.plot.kde(bw_method=3, title="PDF using Kernel Density Estimation - Bandwidth value=3");

plot.show(block=True);

 

Output:

Kernel Density Estimation Plot using pandas DataFrame in Python - Default Bandwidth

Kernel Density Estimation Plot using pandas DataFrame in Python - Bandwidth = 0.3

Kernel Density Estimation Plot using pandas DataFrame in Python - Bandwidth = 3


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