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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 2. Uploading, Downloading, and Manipulating Data FREE CHAPTER 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 tabs

There is a second way to organize your Streamlit app layout that is remarkably similar to the Streamlit column, called the tab. Tabs are useful when you have content that is too wide to break up into columns, even in wide mode, and also are useful when you want to focus attention by only showing one piece of content at a time. For example, if we had three very distinct graphs that only looked good in wide mode, but we didn’t want to put them vertically on top of each other, we could use tabs to selectively show them. Let’s explore exactly how this works!

st.tabs works very similarly to st.columns, but instead of telling Streamlit the number of tabs we want, we instead pass along the names of the tabs and then use now-familiar with statements to place content into the tab. The next bit of code turns the columns from our most recent Streamlit app into tabs:

import pandas as pd
import streamlit as st
 
st.set_page_config(layout="wide&quot...
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