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Python Visualizations & Animations

Python Line Animation

Python Visualization Tutorials

Data visualization is graphical representation of data.

This technique is used in pretty much every field in business because there is always some type of data or statistic to interpret.

You can see the big picture, as well as smallest details more conveniently with visualization. It transforms numbers and relations to trends, colors, shapes and correlations.

Since programming is so powerful and it brings function and automation capabilities it makes sense that visualization is a big practice in Python also.

Data Visualization

Visualization is a term with multiple meanings. Although it refers to the similar phenomenon that stimulates the same visual sensory in the brain, visualization can mean imagining something that’s currently not present. This usage is more common in psychology.

Visualization is often used to mean visualization of data in more technical or business related fields such as coding, research, science, finance etc.

Data Visualization Examples

Visualization can be done in many forms using Python. The range can start from traditional line, bar and scatter charts to more modern charts like hexbin and pcolor and even animated charts!

Stacked Chart Visualization Example with Python Matplotlib

You can find interesting tutorials about Python Animations like below in this visualization series.

Hexbin Chart created with Matplotlib
Visualization with Pcolor Chart (Matplotlib)
We will demonstrate the usage of FuncAnimation function from matplotlib.animation module to animate many different charts. Below you can see an animated line chart example.

Matplotlib Animations

Line Chart Animation (Matplotlib)

Python Animation Tutorials for specific chart types with Matplotlib:

Tutorials for basic animation knowledge with Python and Matplotlib:

Plotly Tutorials with Python

Plotly is a very intuitive charting library in Python. It’s a very high level approach to beautiful visualizations but it can also be highly customized if need be.

Where Plotly truly shines is Plotly visualizations are really beautiful straight off the bat, it is open-source and also website friendly.

Map Chart Created with Plotly and Mapbox using Python

Thanks to native support of JavaScript technology Plotly outputs can be images, javascript or html making it 100% browser friendly.

How to Install Plotly

pip install plotly
pip3 install plotly
A simple visualization created with Plotly

Matplotlib Tutorials with Python

Matplotlib is a less high-level data visualization library but it can be the backbone of a sophisticated charting application because of its very detailed and adjustable nature. But beware, low-level coding also means writing more code even for very basic charting structures. Which can be very useful or painful depending on what you need.

In fact there are more user friendly libraries that are built on matplotlib for Python such as Seaborn which allows achieving very beautiful end results with very little code.

Matplotlib is known as the fundamental data visualization library for Python.

How to Install Matplotlib

Matplotlib is a native Python library so it doesn’t require separate installation.

Visualization Resources

For additional resources you can check out websites of main data visualization libraries. Here are a few that come to mind:

Uber published a very interesting presentation on Uber’s Data with lots of Visualization examples which you can watch here:

They seem to use MapBox and React for Visualization which seems like a similar JavaScript alternative to Plotly.

Aside of Matplotlib and Plotly, you can also use Pandas, the powerful data frame library in Python applications, to visualize data through data frames.


We have introduced quite a few sophisticated Python Visualization libraries and Visualization tutorials.

We have also shared additional resources that can compliment your learning and we made some definitions regarding data visualization.

Using our Python Tutorials you can explore the world of animated charts as well as visualization with traditional still charts.