Matplotlib VS Seaborn

Matplotlib and Seaborn are both powerful Python libraries used for data visualization but they have different strengths that are suited for different purposes.

What is Matplotlib?

Matplotlib is a comprehensive and widely used Python library for creating static, interactive and publication-quality visualizations. It provides a versatile toolkit for generating various types of plots and charts which makes it an essential tool for data scientists, researchers, engineers and analysts. The following are the features of the matplotlib library.

Core Library

Matplotlib is the foundational library for plotting in Python. It provides low-level control over visualizations by allowing users to create a wide variety of plots from basic to highly customize.

Customization

It offers extensive customization options by allowing users to control every aspect of a plot. This level of control can sometimes result in more code for creating complex plots.

Basic Plotting

While it's highly flexible for creating certain complex plots might require more effort and code compared to specialized libraries like Seaborn.

Simple plot by matplotlib

The following is the simple line plot created by using the matplotlib lbrary pyplot module.

Example

import matplotlib.pyplot as plt
# Creating a plot
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])

# Customizing axis limits and labels
plt.xlim(0, 5)
plt.ylim(0, 35)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Output

What is Seaborn?

Seaborn is a Python data visualization library that operates as an abstraction layer over Matplotlib. It's designed to create visually appealing and informative statistical graphics, simplifying the process of generating complex visualizations from data. The following are the key features of the seaborn library.

Statistical Data Visualization

Seaborn is built on top of Matplotlib and is particularly well-suited for statistical data visualization. It simplifies the process of creating complex plots by providing high-level abstractions.

Default Aesthetics

Seaborn comes with attractive default styles and color palettes that make plots aesthetically pleasing with minimal effort.

Specialized Plots

It specializes in certain types of plots like violin plots, box plots, pair plots and more which are easier to create in Seaborn compared to Matplotlib.

Basic seaborn plot

The following is the basic seaborn line plot.

Example

import seaborn as sns
import matplotlib.pyplot as plt
# Sample data
x_values = [1, 2, 3, 4, 5]
y_values = [2, 4, 6, 8, 10]

# Creating a line plot using Seaborn
sns.lineplot(x=x_values, y=y_values)
plt.show()

Output

	Matplotlib	Seaborn
Level of Abstraction	Matplotlib is more low-level and requires more code for customizations.	Seaborn abstracts some complexities by enabling easier creation of complex statistical plots.
Default Styles	Matplotlib doesn’t have better default styles and color palettes when compared to seaborn.	Seaborn has better default styles and color palettes by making its plots visually appealing without much customization.
Specialized Plots	Matplotlib require more effort to plot certain plots readily.	Seaborn offers certain types of plots that are not readily available or require more effort in Matplotlib.
When to use each library	We can use this library when we need fine-grained control over the appearance of our plots or when creating non-standard plots that may not be available in other libraries.	We can use this library when working with statistical data especially for quick exploration and visualization of distributions, relationships and categories within the data. Seaborn's high-level abstractions and default styles make it convenient for this purpose.

Both libraries are valuable in their own way and sometimes they can be used together to combine the strengths of both for advanced visualization tasks.

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