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

Using Streamlit Components – streamlit-pandas-profiling

pandas-profiling is a very powerful Python library that automates some of the EDA that is often the first step in any data analysis, modeling, or even data engineering task. Before a data scientist begins almost any data work, they want to start with a good understanding of the distributions of their underlying data, the number of missing rows, correlations between variables, and many other basic pieces of information. As we mentioned before, this library automates the process and then places this interactive analytics document into a Streamlit app for the user. 

Behind the Streamlit component called pandas-profiling, there is a full Python library with the same name that the component imports its functions from. The Streamlit component here actually renders the output from the pandas-profiling Python library in a way that becomes very easy to integrate. For this segment, we will first learn how to implement the...