# Types of Plots

Now that we've covered the different libraries that we'll explore, let's take a bit of time to discuss some of the plots and visualizations you can create with these libraries. As discussed in the section introducing the different libraries, Python can be used to visualize everything from simple, static graphs and plots to complex, interactive, and even 3D plots.

Covering the variety of plots you can create will help you get a better idea of different ways you can visualize your data and how to choose the right plot type for the job.

We’ll divide the plot types into several categories:

**Statistical plots****Images****Networks/Graphs****Geographical****3D and Interactive****Grids and Meshes**

### Statistical Plots

Statistical plots are, arguably, the most common type of plots you'll come across. These plots are commonly used in statistics to visualize datasets, making comparisons between different features and observing trends in the data.

They are generally simple and really useful for basic descriptive statistics and data analysis. Some of these plots are learned in elementary school and fall under the category of common knowledge, though some, like *Scatterplots* and *Violin Plots* might be unfamiliar to you without prior knowledge on the subject.