Data Visualization is the graphic representation of data. It converts a huge dataset into small graphs, thus aids in data analysis and predictions. It is an indispensable element of data science which 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 statical interferences and plotting 2D graphs of arrays. It was first introduced by John D. Hunter in 2002. It uses Pyplot for providing 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 difference between Matplotlib and Seaborn

Features | Matplotlib | Seaborn |
---|---|---|

Functionality |
It is utilized for making basic graphs. Datasets are visualised with the help of bargraphs, histograms, piecharts, scatter plots, lines and so on. | Seaborn contains a number of patterns and plots for data visualization. It uses fascinating themes. It helps in compiling whole data into a single plot. It also provides distribution of data. |

Syntax |
It uses comparatively complex and lengthy syntax. Example: Syntax for bargraph- 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 Figures |
We 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 time for the creation of each figure. However, it may lead to (OOM) out of memory issues |

Visualization |
Matplotlib 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. |

Pliability |
Matplotlib is a highly customized and robust | Seaborn avoids overlapping of plots with the help of its default themes |

Data Frames and Arrays |
Matplotlib works efficiently with data frames and arrays.It treats figures and aces 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 Cases |
Matplotlib plots various graphs using Pandas and Numpy | Seaborn is the extended version of Matplotlib which uses Matplotlib along with Numpy and Pandas for plotting graphs |

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