--- title: "Matplotlib vs. Seaborn" date: "2022-10-31" categories: - "python-math-and-science" coverImage: "iris_dataset_plot.png" --- # Matplotlib vs. Seaborn Visualizing your data makes it easier for people to understand it. Visualization helps in the analysis of datasets and the extraction of insights. Matplotlib and Seaborn are complete toolkits for producing static, animated, and interactive visualizations in Python. - Seaborn is more comfortable handling Pandas data frames and uses basic methods to provide beautiful graphics in Python. - Matplotlib works efficiently with data frames and arrays, treats figures and axes as objects, and contains various stateful APIs for plotting. In this article, we learn step by step how we can plot and label the graphs in Matplotlib and Seaborn. Moreover, we will also compare various plots using Seaborn and Matplolib modules. We assume you have a basic idea of Python visualization and Jupyter notebook. If you haven't installed Jupyter notebook yet, we highly recommend installing it on your system since the implementation part in the article will be in using it. ## Comparing Matplotlib and Seaborn The two most important data visualization libraries in Python are Matplotlib and Seaborn. - Matplotlib is a Python library used to plot various graphs with the help of additional libraries like Numpy and Pandas. It is an effective Python tool for data visualization and is mainly used to plot 2D graphs of arrays. Moreover, it also uses Pyplot to offer a free and open-source MATLAB-like interface. It can work with different operating systems and their graphical front ends. - Seaborn is also a Python library that utilizes Matplotlib, Pandas, and Numpy to plot graphs. It is a superset of the Matplotlib library and is constructed on top of it. It helps in the visualization of univariate and bivariate data. Moreover, you can use it to create static Time-Series data graphs. The following table compares the Matplotlib and Seaborn modules:
Matplotlib | Seaborn |
Matplotlib creates simple graphs, including bar graphs, histograms, pie charts, scatter plots, lines, and other visual representations of data. | There are numerous patterns and graphs for data visualization in Seaborn. It employs engaging themes, and it helps in the integration of all data into a single plot. Additionally, it offers data distribution. |
It utilizes syntax that is relatively complicated and extensive. Example: Matplotlib.pyplot.bar(x-axis, y-axis) is the syntax for a bar graph. | It has relatively simple syntax, making it simpler to learn and comprehend. Example: seaborn.barplot(x axis, y-axis) syntax for a bar graph. |
We can open and work with many figures at once. You can close the current figure using the syntax matplotlib.pyplot.close(). Close all the figures using this syntax: matplotlib.pyplot.close("all") | Seaborn sets the time for the creation of each figure. However, it may lead to (OOM) memory issues. |
Matplotlib is a Python graphics package for data visualization and integrates nicely with Numpy and Pandas. Similar capabilities and syntax are available in Pyplot as in MATLAB, and users of MATLAB can readily understand it. | Seaborn is more comfortable with Pandas data frames. It utilizes simple sets of techniques to produce lovely images in Python. |
Matplotlib is highly customized and robust. | With the help of its default themes, Seaborn prevents overlapping plots. |
Matplotlib plots various graphs using Pandas and Numpy. | Seaborn is the extended version of Matplotlib, which uses Matplotlib, Numpy, and Pandas to plot graphs. |