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

Using Streamlit Components – streamlit-embedcode

If we want to display code on Streamlit, we can easily just treat the code as text and use the familiar st.write(), which takes text as input, or st.markdown(), which takes markdown as input. This might work well for small snippets but will be a struggle to format easily, and may not look good for the average user or longer bits of code. As a result, streamlit-embedcode was created to help solve this problem. 

Showing snippets of code to others is a commonly solved problem; a few solutions that are out there include sharing snippets with GitHub gists (which are like mini GitHub repositories with only one text file) with GitLab snippets (which are the same as gists but for GitLab) and using Pastebin, which is a shared text/code snippets freeway outside of GitHub/GitLab. Now, we can make a Python file with some example Streamlit code, put it in a GitHub gist, and call it from a new Streamlit app. To do so, we'll...