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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

Connecting to Snowflake with Streamlit

To connect to any database within Streamlit, we mostly need to think about how to connect to that service in Python and then add some Streamlit-specific functions (like caching!) to improve the user experience. Luckily, Snowflake has invested a lot of time in making it incredibly easy to connect to Snowflake from Python; all you need to do is specify your account info and the Snowflake Python connector does the rest.

In this chapter, we’ll create and work in a new folder called database_examples and add a streamlit_app.py file, along with a Streamlit secrets file to get started:

mkdir database_examples
cd database_examples
touch streamlit_app.py
mkdir .streamlit
touch .streamlit/secrets.toml

Within the secrets.toml file, we need to add our username, password, account, and warehouse. Our username and password are the ones we added when we signed up for our Snowflake account, the warehouse is the virtual computer that Snowflake...

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