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)
1
Section 1: Creating Basic Streamlit Applications
7
Section 2: Advanced Streamlit Applications
11
Section 3: Streamlit Use Cases

Understanding ML results

So far, our app might be useful, but often just showing a result is not good enough for a data app. We also should show some explanation as to why they got the result that they did! In order to do this, we can include in the output of the app that we have already made a section that helps users understand the model better.

To start, random forest models already have a built-in feature importance method derived from the set of individual decision trees that make up the random forest. We can edit our penguins_ml.py file to graph this importance, and then call that image from within our Streamlit app. We could also graph this directly from within our Streamlit app, but it is more efficient to make this graph once in penguins_ml.py instead of every time our Streamlit app reloads (which is every time a user changes a user input!). The following code edits our penguins_ml.py file and adds the feature importance graph, saving it to our folder. We also call the...