Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
Table of Contents (17 chapters)
Section 1: Creating Basic Streamlit Applications
Section 2: Advanced Streamlit Applications
Section 3: Streamlit Use Cases

Streamlit's built-in visualization options

For the rest of this chapter, we're going to run through the rest of the Streamlit visualization options, which are Plotly, Matplotlib, Seaborn, Bokeh, Altair, and PyDeck


Plotly is an interactive visualization library that many data scientists use for visualizing data in Jupyter, in the browser locally, or even hosting these graphs to be viewed on a platform for visualizations and dashboards created by the Plotly team called Dash. This library is very similar to Streamlit in its intent and is primarily used for internal or external dashboards (hence, the name Dash). 

Streamlit allows us to call plotly graphs from within Streamlit apps using the st.plotly_chart() function, which makes it a breeze to port any Plotly or Dash dashboards. We'll test this out by making a histogram of the height of the trees in SF, essentially the same graph that we've made before. The following code makes our Plotly...