Difference Between Matplotlib VS Seaborn Last Updated : 06 Mar, 2024 Comments Improve Suggest changes Like Article Like Report Data Visualization is the graphic representation of data. It converts a huge dataset into small graphs, thus aiding in data analysis and predictions. It is an indispensable element of data science that makes complex data more understandable and accessible. Matplotlib and Seaborn act as the backbone of data visualization through Python. Matplotlib: It is a Python library used for plotting graphs with the help of other libraries like Numpy and Pandas. It is a powerful tool for visualizing data in Python. It is used for creating statistical inferences and plotting 2D graphs of arrays. It was first introduced by John D. Hunter in 2002. It uses Pyplot to provide a MATLAB-like interface free and open-source. It is capable of dealing with various operating systems and their graphical backends. Seaborn: It is also a Python library used for plotting graphs with the help of Matplotlib, Pandas, and Numpy. It is built on the roof of Matplotlib and is considered as a superset of the Matplotlib library. It helps in visualizing univariate and bivariate data. It uses beautiful themes for decorating Matplotlib graphics. It acts as an important tool in picturing Linear Regression Models. It serves in making graphs of statical Time-Series data. It eliminates the overlapping of graphs and also aids in their beautification. Table of differences between Matplotlib and SeabornFeaturesMatplotlibSeabornFunctionalityIt is utilized for making basic graphs. Datasets are visualized with the help of bar graphs, histograms, pie charts, scatter plots, lines, and so on.Seaborn contains several patterns and plots for data visualization. It uses fascinating themes. It helps in compiling whole data into a single plot. It also provides the distribution of data.SyntaxIt uses comparatively complex and lengthy syntax. Example: Syntax for bar graph- matplotlib.pyplot.bar(x_axis, y_axis).It uses comparatively simple syntax which is easier to learn and understand. Example: Syntax for bargraph- seaborn.barplot(x_axis, y_axis).Dealing Multiple FiguresWe can open and use multiple figures simultaneously. However, they are closed distinctly. Syntax to close one figure at a time: matplotlib.pyplot.close(). Syntax to close all the figures: matplotlib.pyplot.close("all")Seaborn sets the time for the creation of each figure. However, it may lead to (OOM) out of memory issuesVisualizationMatplotlib is well connected with Numpy and Pandas and acts as a graphics package for data visualization in Python. Pyplot provides similar features and syntax as in MATLAB. Therefore, MATLAB users can easily study it.Seaborn is more comfortable in handling Pandas data frames. It uses basic sets of methods to provide beautiful graphics in Python.PliabilityMatplotlib is a highly customized and robustSeaborn avoids overlapping plots with the help of its default themesData Frames and ArraysMatplotlib works efficiently with data frames and arrays.It treats figures and axes as objects. It contains various stateful APIs for plotting. Therefore plot() like methods can work without parameters.Seaborn is much more functional and organized than Matplotlib and treats the whole dataset as a single unit. Seaborn is not so stateful and therefore, parameters are required while calling methods like plot()Use CasesMatplotlib plots various graphs using Pandas and NumpySeaborn is the extended version of Matplotlib which uses Matplotlib along with Numpy and Pandas for plotting graphs Comment More infoAdvertise with us Next Article Difference Between Matplotlib VS Seaborn hacksight Follow Improve Article Tags : Python Difference Between Python-matplotlib Python-Seaborn Practice Tags : python Similar Reads Data Visualisation in Python using Matplotlib and Seaborn It may sometimes seem easier to go through a set of data points and build insights from it but usually this process may not yield good results. There could be a lot of things left undiscovered as a result of this process. Additionally, most of the data sets used in real life are too big to do any an 14 min read Rotate axis tick labels in Seaborn and Matplotlib Seaborn and Matplotlib both are commonly used libraries for data visualization in Python.  We can draw various types of plots using Matplotlib like scatter, line, bar, histogram, and many more. On the other hand, Seaborn provides a variety of visualization patterns. It uses easy syntax and has easil 3 min read Customizing Styles in Matplotlib Here, we'll delve into the fundamentals of Matplotlib, exploring its various classes and functionalities to help you unleash the full potential of your data visualization projects. From basic plotting techniques to advanced customization options, this guide will equip you with the knowledge needed t 12 min read Matplotlib.figure.Figure.show() in Python Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The figure module provides the top-level Artist, the Figure, which contains all the plot elements. This module is used to control the default spacing of the subplots and top level container for all plot 2 min read How can I show figures separately in Matplotlib? In data visualization, it’s common to create multiple plots or figures for comparison, but sometimes you may need to display each plot separately. In Matplotlib, this can be easily achieved by creating and managing multiple figure objects. Most common method to create a new figure, or activate an ex 2 min read Matplotlib.figure.Figure() in Python Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The figure module provides the top-level Artist, the Figure, which contains all the plot elements. This module is used to control the default spacing of the subplots and top level container for all plot 2 min read Adding Grid lines to a Catplot in Seaborn In Seaborn, catplot is a function mainly for visualizing categorical data. It combines a categorical plot with a FacetGrid, allowing us to explore relationships between variables across different categories. While catplot provides informative visualizations by default, adding grid lines can further 4 min read Relational plots in Seaborn - Part I Relational plots are used for visualizing the statistical relationship between the data points. Visualization is necessary because it allows the human to see trends and patterns in the data. The process of understanding how the variables in the dataset relate each other and their relationships are t 4 min read Matplotlib - Practice, Exercise, and Solutions Python Matplotlib is a library for visualization that helps to create a variety of charts in a variety of hardcopy formats. You might have seen various Matplotlib tutorials but the best way to gain a command over this library is by practicing more and more. This Matplotlib exercise helps you learn M 5 min read Relational plots in Seaborn - Part II Prerequisite: Relational Plots in Seaborn - Part IIn the previous part of this article, we learnt about the relplot(). Now, we will be reading about the other two relational plots, namely scatterplot() and lineplot() provided in seaborn library. Both these plots can also be drawn with the help of ki 6 min read Like