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

San Francisco Trees – A new dataset

We're going to be working with all sorts of graphs in this chapter, so we're going to need a new dataset that has much more info, especially dates and locations. Enter SF Trees. The department of public works in San Francisco has a dataset (cleaned by the wonderful folks in the R community who run Tidy Tuesday, a weekly event where people publish interesting visualizations of new data each week) of every tree planted and maintained in the city of San Francisco. They cleverly call this dataset EveryTreeSF – Urban Forest map, and update this dataset every day. I have selected a random 10,000 trees with complete info and placed this data in the main GitHub repository under the trees folder (I'm not as clever as the data engineer in SF's DPW, I know). The GitHub repo can be found at If you would like to download the full dataset, the link is here: https://data.sfgov...