At this point, we have already covered the basics of creating and customizing plots using Matplotlib. In this chapter, we begin the journey of understanding more advanced Matplotlib usage through examples in specialized topics.

When considering the visualization of a concept, the following important factors have to be considered carefully:

• Source of the data
• Filtering and data processing
• Choosing the right plot type for the data:
  • Visualizing the trend of data:
    • Line chart, area chart, and stacked area chart
  • Visualizing univariate distribution:
    • Bar chart, histogram, and kernel density estimation
  • Visualizing bivariate distribution:
    • Scatter plot, KDE density chart, and hexbin chart
  • Visualizing categorical data:
    • Categorical scatter plot, box plot, swarm plot, violin plot
• Adjusting figure aesthetics for effective storytelling

We will cover these topics via the use of demographic and financial data. First, we will discuss typical data formats when we fetch data from the Application Programming Interface (API). Next, we will explore how we can integrate Matplotlib 2.0 with other Python packages such as Pandas, Scipy, and Seaborn for the visualization of different data types.