数据可视化是数据的图形表示。它将庞大的数据集转换为小图，从而有助于数据分析和预测。它是数据科学不可或缺的元素，它使复杂的数据更易于理解和访问。 Matplotlib 和 Seaborn 通过Python充当数据可视化的支柱。
Matplotlib：它是一个Python库，用于在 Numpy 和 Pandas 等其他库的帮助下绘制图形。它是在Python可视化数据的强大工具。它用于创建静态干扰和绘制阵列的二维图形。它由 John D. Hunter 于 2002 年首次推出。它使用 Pyplot 来提供类似 MATLAB 的免费和开源接口。它能够处理各种操作系统及其图形后端。
Seaborn：它也是一个Python库，用于在 Matplotlib、Pandas 和 Numpy 的帮助下绘制图形。它建立在 Matplotlib 的屋顶上，被认为是 Matplotlib 库的超集。它有助于可视化单变量和双变量数据。它使用漂亮的主题来装饰 Matplotlib 图形。它是描绘线性回归模型的重要工具。它用于制作静态时间序列数据的图表。它消除了图形的重叠并有助于美化它们。
Matplotlib 和 Seaborn 的区别表
| 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 |