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 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);

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