Chapter 3: Data Visualization
Visualization is fundamental to the modern data scientist. It is often the central lens used to understand items such as statistical models (for example, via an AUC chart), the distribution of a crucial variable (via a histogram), or even important business metrics.
In the last two chapters, we used the most popular Python graphing libraries (Matplotlib and Seaborn) in our examples. This chapter will focus on extending that ability to a broad range of Python graphing libraries, along with including some graphing functions native to Streamlit.
By the end of this chapter, you should feel comfortable with using Streamlit's native graphing functions, and also using Streamlit's visualization functions to place graphs made from major Python visualization libraries in your own Streamlit app.
In this chapter, we will cover the following topics:
- San Francisco Trees – A new dataset
- Streamlit's built-in graphing...
