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

Setting up the SF Trees dataset

For this chapter, we will be working with the SF Trees dataset again, the same dataset that we used in Chapter 3, Data Visualization. As we did in the previous chapters, we need to follow this list of steps for the setup:

  1. Create a new folder for the chapter.
  2. Add our data to the folder.
  3. Create a Python file for our app.

Let's see each of these steps in detail.

In our main streamlit_apps folder, run the following code in your terminal to make a new folder cleverly called pretty_trees. You can also create a new folder manually outside the terminal:

mkdir pretty_trees

Now, we need to move our data from Chapter 3, Data Visualization, into our folder for this chapter. The following code copies the data into the correct folder: 

cp trees_app/trees.csv pretty_trees

If you do not have the trees_app folder and have not yet completed Chapter 3, Data Visualization, you can also download the necessary data from https...