Book Image

Streamlit for Data Science - Second Edition

By : Tyler Richards
3.3 (3)
Book Image

Streamlit for Data Science - Second Edition

3.3 (3)
By: Tyler Richards

Overview of this book

If you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.
Table of Contents (15 chapters)
13
Other Books You May Enjoy
14
Index

Multi-page Apps

Our Streamlit apps thus far have all been single pages, where all or nearly all the information in the app has been visible to us with a simple scroll. However, Streamlit also has multi-page functionality. Multi-page apps are a powerful tool for creating apps that are not limited to one page of content, and can extend that user experience that comes with Streamlit.

For our first application of multi-page apps, we’re going to focus on separating the map section of the trees app from the rest of the graphs in two separate apps. The way Streamlit creates multi-page apps is it looks in the same directory as our Streamlit app for a folder called pages, and then runs each Python file inside the pages folder as its own Streamlit app. To do this, create a new folder inside pretty_trees called pages, and then a files inside pages called map.py and app2.py. In your terminal from the base folder in the repository, you can run the following.

mkdir pages
touch pages/map.py
...