- How to create Plotly animations
- How to save Plotly animations
- How to create Mapcharts – Plotly & Mapbox
- How to create Matplotlib animations
- How to save Matplotlib animations
- Titles, Axes, Ticks & Legend
- Matplotlib Built-in Styles
- Animated Line Charts
- Multi-line Animation
- Stacked Area Charts
- Word Cloud Visualization
- Bar Chart Animations
- Colors with Python
- Creating Multiple Charts
- Creating Hexbin Charts
- Creating Pcolor Charts
- Creating Bokeh Scatter Charts
Python Visualizations & Animations
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!
You can find interesting tutorials about Python Animations like below in this visualization series.
Matplotlib Animations
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.
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
or
pip3 install 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.
- A Guide to Matplotlib’s Built-in Styles
- Colors with Python
- Titles, Axes, Ticks & Legend
- Stacked Area Charts
- Word Cloud Visualization
- Creating Multiple Charts
- Creating Hexbin Charts
- Creating Pcolor Charts
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.
Summary
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.