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Getting Started with Streamlit for Data Science

You're reading from   Getting Started with Streamlit for Data Science Create and deploy Streamlit web applications from scratch in Python

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800565500
Length 282 pages
Edition 1st Edition
Languages
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Author (1):
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Tyler Richards Tyler Richards
Author Profile Icon Tyler Richards
Tyler Richards
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Creating Basic Streamlit Applications
2. Chapter 1: An Introduction to Streamlit FREE CHAPTER 3. Chapter 2: Uploading, Downloading, and Manipulating Data 4. Chapter 3: Data Visualization 5. Chapter 4: Using Machine Learning with Streamlit 6. Chapter 5: Deploying Streamlit with Streamlit Sharing 7. Section 2: Advanced Streamlit Applications
8. Chapter 6: Beautifying Streamlit Apps 9. Chapter 7: Exploring Streamlit Components 10. Chapter 8: Deploying Streamlit Apps with Heroku and AWS 11. Section 3: Streamlit Use Cases
12. Chapter 9: Improving Job Applications with Streamlit 13. Chapter 10: The Data Project – Prototyping Projects in Streamlit 14. Chapter 11: Using Streamlit for Teams 15. Chapter 12: Streamlit Power Users 16. Other Books You May Enjoy

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...

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