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

Deploying with Streamlit Sharing

Now that all our necessary files are in the GitHub repository, we have almost all that we need to deploy our application. You can use the following list of steps to deploy our application:

  1. When we deploy to Streamlit Sharing, Streamlit uses its own servers to host the app. Because of this, we need to explicitly tell Streamlit which Python libraries are required for our app to run. The following code installs a very helpful library called pipreqs and creates a requirements.txt file in the format we need for Streamlit: 
    pip install pipreqs
    pipreqs .
  2. When we look at our requirements.txt file, we can see that pipreqs looked through all of our Python files and checked what we imported and used, and created a file that Streamlit can use to install the exact same versions of our libraries to prevent errors: 

    Figure 5.3 – Requirements.txt

  3. We have a new file, so we need to also add it to our GitHub repository. The following code...