How to Form a Polars DataFrame from SQL Alchemy Asynchronous Query Result

Learn how to convert SQL Alchemy asynchronous query results into a `Polars DataFrame` effortlessly and avoid common pitfalls in data extraction.
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How to Form a Polars DataFrame from SQL Alchemy Asynchronous Query Result

If you're working with asynchronous database queries in Python using SQL Alchemy, you may find yourself in a situation where you need to convert query results into a Polars DataFrame. This task can seem daunting, especially when you want the process to be dynamic—that is, avoiding hardcoding column names. In this guide, we will walk through the necessary steps to achieve this seamlessly.

Understanding the Problem

You have successfully executed an asynchronous SQL query using SQL Alchemy, retrieving data from the User table. However, you want to construct a Polars DataFrame from these results without manually inputting the column names. Additionally, you seek to know how to mark the SQL Alchemy session as read-only to prevent any data corruption when no write operations are intended.

The initial steps you took were correct, but you encountered issues with retrieving the actual column names from the SQL Alchemy result object. Let’s break down how to effectively convert that data into a Polars DataFrame.

Step-by-Step Solution

Step 1: Execute Your Select Query

First, you need to create your select query. You’ve already done this using the following code:

[[See Video to Reveal this Text or Code Snippet]]

This query retrieves the latest 10 users ordered by their login date in descending order.

Step 2: Compile the SQL Statement

Next, to facilitate fetching the result into Polars, it’s best to convert your select statement into a string representation. This can be accomplished with the following code:

[[See Video to Reveal this Text or Code Snippet]]

This will generate a string equivalent of your SQL query, which is more compatible with the Polars API.

Step 3: Import and Utilize Polars to Read the Database

Now comes the main part—using Polars to read the database using the compiled SQL statement. Here’s how you do it:

[[See Video to Reveal this Text or Code Snippet]]

This code snippet uses the exported SQL statement to immediately fetch the result into a Polars DataFrame, maintaining flexibility regarding column names. Polars handles the dynamic nature of data extraction, which is exactly what you need.

Marking the Session as Read-Only

To ensure that your session is read-only and to prevent any accidental data modifications, when creating the SQL Alchemy session, use the isolation_level parameter. Here's how you can do it:

[[See Video to Reveal this Text or Code Snippet]]

By setting the isolation_level to AUTOCOMMIT, you help prevent any unintended write operations during this session.

Conclusion

By following these steps, you can dynamically form a Polars DataFrame from SQL Alchemy asynchronous query results without the hassle of hardcoding column names. Furthermore, marking your SQL Alchemy session as read-only helps safeguard your data integrity. Experiment with this approach in your Python applications, and streamline your data manipulation processes!

Feel free to reach out if you encounter any issues or have questions—I’d love to help!

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