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

Picking colors with Color Picker

Colors are very difficult to take in as user input in applications. If a user wants red, do they want light red or dark red? Maroon or a pinkish red? Streamlit's approach to this problem is st.color_picker(), which lets the user pick a color, and returns that color in a hex string (which is a unique string that defines very specific color shades used by most graphing libraries as input). The following code adds this color picker to our previous app and changes the color of the Seaborn graphs to be based on the color that the user selects: 

import streamlit as st
import pandas as pd
import seaborn as sns
import datetime as dt
import matplotlib.pyplot as plt
st.title('SF Trees')
st.write('This app analyses trees in San Francisco using'
         ' a dataset kindly provided by SF DPW. The '
         'histogram below is...