Seaborn

 Visualize Distributions Using Seaborn

Seaborn is a powerful Python visualization library built on top of Matplotlib. It simplifies the process of creating attractive and informative statistical graphics, including plots for visualizing data distributions.

Installing Seaborn

If you already have Python and pip installed, you can install Seaborn by running:

pip install seaborn

If you are working in Jupyter Notebook, use:

!pip install seaborn

Creating Distribution Plots (Displots)

A displot (short for distribution plot) is used to visualize the distribution of a dataset. It can display histograms, kernel density estimates (KDE), or both. Import Required Libraries

Importing Matplotlib

To use Matplotlib for plotting, import the pyplot module with the following command:

import matplotlib.pyplot as plt

Import Seaborn

To import the Seaborn library into your code, use the following command:

import seaborn as sns

Basic Displot Example (Histogram)

import matplotlib.pyplot as plt

import seaborn as sns

sns.displot([0, 1, 2, 3, 4, 5])

plt.show()

Output:

The output will be a histogram plot:

The histogram will have bins representing the counts of values in the list [0, 1, 2, 3, 4, 5].

Since all numbers appear once, each bar will have a height of 1 for each number (each value appears only once).

The x-axis will range from around 0 to 5.

The y-axis will range from 0 to 1 or slightly higher depending on binning behavior.

Displot with KDE Curve (Without Histogram)

To plot only the KDE (Kernel Density Estimate) curve without the histogram, use the kind="kde" argument:

import matplotlib.pyplot as plt

import seaborn as sns

sns.displot([0, 1, 2, 3, 4, 5], kind="kde")

plt.show()

Output:

The output will be a smooth KDE curve (similar to a smoothed histogram) representing the distribution of the given numbers [0, 1, 2, 3, 4, 5].

Since these numbers are evenly spaced, the KDE plot will look like a smooth bump rising from near 0 and peaking around the middle of the range, then decreasing.

There is no text output, just a graphical plot.

Note:

In this tutorial, we’ll use:

sns.displot(arr, kind="kde")

to visualize random distributions.