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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 BigQuery with Streamlit

The first step to getting BigQuery connected to your Streamlit app is to gather the authentication information necessary from BigQuery. There is a wonderful Quickstart doc that Google keeps (and maintains!) that you should follow, which can be found here: https://cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries. This link will help you sign up for a free account, and create a project. After you create your project, you need to create a service account (https://console.cloud.google.com/apis/credentials) and download the credentials as a JSON file. Once you have this file, you have all the data needed and can return to this chapter.

For this section, we will create a new file in our database_example folder called bigquery_app.py, and we will add a new section to the secrets.toml file we already created. First, we can add to the secrets.toml file and finally, let you create and view your service account credentials using...

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