Information visualization, as David McCandless aptly puts it, is a form of "knowledge compression." Our highly evolved visual processing system enables us to efficiently handle vast amounts of information. Visualization's power lies in its ability to encode data intuitively, making complex data accessible. As data grows in volume and complexity, the importance of effective visualization increases. This video explores how the human visual cortex processes colors and shapes and how we can utilize these mechanisms for effective visualization using Pythons powerful visualization libraries.
Starting with pandas and Matplotlib, two core Python libraries, we learn about the basics of Python data pre-processing and visualization before moving on to more advanced packages. Seaborn, built on top of Matplotlib, simplifies common tasks and enhances productivity. Interactive visualizations using Bokeh and Plotly are also explored. We’ll use Jupyter notebooks to craft our visualizations.
- Understanding Color Theory
- Overview of Human Vision
- Color Schemes
- Understand the Fundamental Principles of Analytical Design
- Describe the Fundamental Tools of Visualization
- Advantages and Disadvantages of Different Chart Types
- DataFrames and Series
- GroupBy and Pivot Tables
- Merge and Join
- The Plot Function
- Demo
- Time Series
- Bar Plot Demo
- Fundamental Components of a matplotlib plot
- Explore the matplotlib API
- Demo
- Stylesheets
- Demo
- Mapping
- Demo
- Matploltib Animation API
- Func Animation
- Animation Writers
- Demo
- ipywidgets as Interactive Browser Controls
- Simple Wdget Use
- Widget Customization
- Demo
- Understand the Structure of seaborn
- Understand the Differences with matplotlib
- Explore the Seaborn API
- Demo
- Basic Plotting with Bokeh
- Advanced Plotting
- Networks
- Demo
- Basic Plotly
- 3D and Animated Plots
- Demo