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

Exploring page configuration

Streamlit allows us to configure a few essential page-specific features at the top of each Streamlit app. So far, we have been using the Streamlit defaults, but at the top of our Streamlit app, we can manually configure everything, from the page title shown on the web browser used to open our Streamlit apps, to the page layout, to the sidebar default state (we will cover the sidebar in the Using the Streamlit sidebar section!). 

The default for Streamlit apps is to have a centered page layout, which is why there is copious white space on the edges of our apps. The following code sets up our Streamlit app in a wide format instead of our default centered one:

import streamlit as st
import pandas as pd
st.title('SF Trees')
st.write('This app analyses trees in San Francisco using'
         ' a dataset kindly provided by SF DPW'...