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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

Streamlit plotting demo

First, we're going to start to learn how to make Streamlit apps by reproducing the plotting demo we saw before in the Streamlit demo, with a Python file that we've made ourselves. In order to do that, we will do the following:

  1. Make a Python file where we will house all our Streamlit code.
  2. Use the plotting code given in the demo.
  3. Make small edits for practice.
  4. Run our file locally.

Our first step is to create a folder called plotting_app, which will house our first example. The following code makes this folder when run in the terminal, changes our working directory to plotting_app, and creates an empty Python file we'll call plot_demo.py:

mkdir plotting_app
cd plotting_app
touch plot_demo.py

Now that we've made a file called plot_demo.py, open it with any text editor (if you don't have one already, I'm partial to VS Code (https://code.visualstudio.com/download). When you open it up, copy and paste the...

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