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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

Making an app from scratch

Now that we've tried out the apps others have made, let's make our own! This app is going to focus on using the central limit theorem, which is a fundamental theorem of statistics that says that if we randomly sample with replacement enough from any distribution, then the distribution of the mean of our samples will approximate the normal distribution.

We are not going to prove this with our app, but instead, let's try to generate a few graphs that help explain the power of the central limit theorem. First, let's make sure that we're in the correct directory (in this case, the streamlit_apps folder that we created earlier), make a new folder called clt_app, and toss in a new file.

The following code makes a new folder called clt_app, and again creates an empty Python file, this time called clt_demo.py:

mkdir clt_app
cd clt_app
touch clt_demo.py

Whenever we start a new Streamlit app, we want to make sure to...

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