Matplotlib vs Seaborn: Which Python Data Visualization Library is Right for You?

Python is a flexible language that may be applied to a number of different data visualization applications. Matplotlib and Seaborn are two well-liked Python libraries for data visualization. Which one ought you use, then?

Data Visualization In Python

The process of developing visual representations for data is known as data visualization. By condensing your data into charts, graphs, and maps, enables you to study and comprehend it in a meaningful way.

Python Libraries

In order to avoid having to reinvent the wheel, developers can accomplish a variety of activities using Python libraries, which are collections of Python packages. What would have taken you weeks to develop from scratch can be accomplished with one or two lines of code. Additionally, Python already has a few pre-installed libraries.

• Matplotlib

A general-purpose Python charting library is called Matplotlib. Since 2003, it has offered an object-oriented API for embedding plots into programs utilizing all-purpose GUI toolkits such as Tkinter, wxPython, Qt, or GTK+. The following are some of the capabilities that Matplotlib provides:

1. Plotting interactively in IPython (IPython is an interactive shell)
2. Support for a variety of graph types, including scatter and line plots
3. Matlab syntax to facilitate language switching. If you must use both Python and Matlab for business, this makes it helpful.

• Seaborn

A matplotlib-based Python data visualization library is called Seaborn. It offers a sophisticated drawing tool for creating eye-catching and educational statistical visuals. Seaborn offers a number of features, including:

1. Assistance with the visualization of regression models
2. A choice of color schemes to enhance the visual appeal of your plots
3. Boxplots and violin plots are useful for plotting categorical variables (variables with categories, like gender).

Top 10 Differences between Matplotlib and Seaborn

1. Basic statistical plots are better using Matplotlib, but more complex statistical plots are better with Seaborn.
2. Compared to seaborn, Matplotlib has a less steep learning curve.
3. Compared to Matplotlib, Seaborn offers more appealing default color palettes. However, if you like, matplotlib allows you to build your own color palettes.
4. Seaborn does not support interactive charting from within IPython, although Matplotlib does.
5. Unlike Matplotlib, Seaborn offers routines to plot categorical variables using boxplots and violin plots.
6. Regression model visualization is possible with Seaborn but not with Matplotlib.
7. Compared to seaborn, Matplotlib provides a larger collection of functions. Seaborn, however, is expanding more quickly than Matplotlib.
8. While matplotlib is distributed under the Python Software Foundation License, Seaborn is licensed under the GNU GPL.
9. Seaborn is less popular than Matplotlib.
10. Matplotlib's documentation is inferior to Seaborn's.

Which Data Visualization Library Should You Use?

Which library ought you to use then? You can choose between the two libraries based on a number of aspects, but one of the most important is whether or not you want to modify your graphs. Because it offers more functionality and is simpler to use, matplotlib is a suitable option if you don't care about personalizing your graphs. However, seaborn is a better choice if you want to produce graphs that seem upscale and expert.

Conclusion

In the end, it’s up to you which library you choose. Try out both libraries and see which one feels more comfortable for you. Happy plotting!