Creating a Distribution Plot with Seaborn

Discover how to craft a distribution plot using Seaborn in Python. Ensure Python and the required libraries are installed prior to starting.

Install Necessary Libraries

Install Seaborn and Matplotlib using pip if they’re not already present in your environment:

pip install seaborn matplotlib

Python Code for Distribution Plot

Below is a Python script for creating a distribution plot:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Generating sample data
data = np.random.normal(size=1000)

# Setting the aesthetic style of the plots
sns.set(style="whitegrid")

# Creating the distribution plot
ax = sns.histplot(data, kde=True)

# Customizing the plot with labels
ax.set_title('Distribution Plot')
ax.set_xlabel('Data Values')
ax.set_ylabel('Frequency')

# Displaying the plot
plt.show()

The script leverages several key functions:

  • sns.histplot with kde=True combines a histogram with a Kernel Density Estimate plot.
  • Title and axes labels are set using set_title, set_xlabel, and set_ylabel.
  • plt.show() is called to display the plot.
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To create a plot, save this script as a .py file and run it with a Python interpreter. The plot will visualize a normal distribution of the sample data. You can replace the data variable with your dataset for customized analysis.