Seaborn is a powerful Python library built on top of Matplotlib, designed specifically for statistical data visualization. It offers a high-level interface for drawing attractive and informative statistical graphics, making it easier to visualize complex datasets. With Seaborn, you can quickly create plots such as scatterplots, boxplots, violin plots, heatmaps, and more, all with built-in themes and color palettes that enhance readability. When integrating Seaborn with Streamlit, you can bring these advanced visualizations directly into your dashboards. This allows you to present nuanced statistical insights interactively, making your data stories more compelling and accessible.

When you use Seaborn to create plots in Streamlit, you typically generate the figure using Matplotlib's plotting interface, since Seaborn builds on top of Matplotlib. In the code above, you load a sample dataset, create a scatterplot with Seaborn, and direct the output to a Matplotlib figure and axis. The st.pyplot(fig) function then renders this figure directly in your Streamlit app. This approach allows you to take full advantage of Seaborn's advanced statistical plotting capabilities, while still maintaining seamless integration with your Streamlit dashboard. By displaying Seaborn plots in this way, you can add rich, interactive visualizations to your app that clearly communicate trends, relationships, and distributions within your data.