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Streamlit for Data Science

You're reading from   Streamlit for Data Science Create interactive data apps in Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803248226
Length 300 pages
Edition 2nd 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 (15) Chapters Close

Preface 1. An Introduction to Streamlit FREE CHAPTER 2. Uploading, Downloading, and Manipulating Data 3. Data Visualization 4. Machine Learning and AI with Streamlit 5. Deploying Streamlit with Streamlit Community Cloud 6. Beautifying Streamlit Apps 7. Exploring Streamlit Components 8. Deploying Streamlit Apps with Hugging Face and Heroku 9. Connecting to Databases 10. Improving Job Applications with Streamlit 11. The Data Project – Prototyping Projects in Streamlit 12. Streamlit Power Users 13. Other Books You May Enjoy
14. Index

Using Streamlit Components – streamlit-pandas-profiling

pandas-profiling is a very powerful Python library that automates some of the EDA, which 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, which 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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