Transitioning from Pandas to Polars the straightforward way — by taking a pit stop at SQL.

The key’s out! Polars is the most popular thing on the block, and everybody wants a slice 😎

I recently wrote a post, “The three Reasons I Permanently Switched From Pandas to Polars”, because, well, that is one of the vital common use-cases for selecting up Polars — as a drop-in alternative for Pandas. Nonetheless, despite the fact that that is essentially the most common use-case, transitioning from Pandas to Polars is usually a bit strange given the heavy differences in syntax between the 2.

In my earlier blog post, I discussed how Pandas forces its users to perform data queries in an object-oriented programming approach, while Polars enables its users to perform data queries in a data-oriented programming approach, very like SQL. As such, despite the fact that Polars most frequently serves as a drop-in alternative for Pandas, for those who’re attempting to learn Polars, comparing it to SQL is probably going a much easier start line than comparing it to Pandas. The target of this post is to do exactly that: to check Polars syntax to SQL syntax as a primer for getting up and running with Polars.

On this post, I show a syntax comparison of Polars vs SQL, by first establishing a toy dataset, after which demonstrating a Polars-to-SQL syntax comparison of three increasingly complex queries on that dataset.

Note that this blog post uses Google BigQuery as its SQL dialect.

The toy dataset used throughout this post is a table of orders and a table of consumers for some restaurant:

orders

| order_date_utc | order_value_usd | customer_id |
|----------------|-----------------|-------------|
| 2024-01-02     | 50              | 001         |
| 2024-01-05     | 30              | 002         |
| 2024-01-20     | 44              | 001         |
| 2024-01-22     | 33              | 003         |
| 2024-01-29     | 25              | 002         |

customers

| customer_id | is_premium_customer | name…