Drawing Kernel Density Estimation-KDE Plot Using Pandas Series

Overview:

  • The kernel density estimation plot draws the probability density for a given distribution.
  • The kernel density plot provides vital display of information on data which include:
    • How the data is distributed around the measures of central tendency like mean and median
    • How the distribution is skewed
    • How the distribution is peaked
  • For a distribution present in a pandas Series, the kernel density estimation plot is drawn by calling the function kde() on the plot member of the Series instance.
  • In the same way to plot the kernel density estimation plot for a pandas DataFrame the function kde() can be invoked on the DataFrame.plot member.

 

Example:

# Example Python program to draw a

# Kernel Density Estimation plot

# for a pandas Series

 

import pandas as pds

import matplotlib.pyplot as plt

 

# Interest rate data as a Python list

distribution    = [2.0, 1.75, 2.25, 2.5, 2.25, 2.5, 2.75, 3, 2.75, 2.5];

series          = pds.Series(distribution);

 

# Draw a KDE plot

series.plot.kde(title="KDE plot for interest rates over ten intervals");

 

plt.show(block=True);

Output:

Drawing a Kernel Density Estimate Plot (KDE Plot) for the data present in a pandas Series

 


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