Transitioning from Pandas to Polars the straightforward manner — by taking a pit cease at SQL.
The key’s out! Polars is the most popular factor on the block, and all people needs a slice 😎
I not too long ago wrote a publish, “The three Causes I Completely Switched From Pandas to Polars”, as a result of, nicely, this is without doubt one of the commonest use-cases for selecting up Polars — as a drop-in substitute for Pandas. Nonetheless, although that is the commonest use-case, transitioning from Pandas to Polars could be a bit unusual given the heavy variations in syntax between the 2.
In my earlier weblog publish, I mentioned how Pandas forces its customers to carry out information queries in an object-oriented programming strategy, whereas Polars permits its customers to carry out information queries in a data-oriented programming strategy, very similar to SQL. As such, although Polars most frequently serves as a drop-in substitute for Pandas, for those who’re attempting to study Polars, evaluating it to SQL is probably going a a lot simpler place to begin than evaluating it to Pandas. The target of this publish is to do exactly that: to check Polars syntax to SQL syntax as a primer for getting up and working with Polars.
On this publish, I present a syntax comparability of Polars vs SQL, by first establishing a toy dataset, after which demonstrating a Polars-to-SQL syntax comparability of three more and more complicated queries on that dataset.
Observe that this weblog publish makes use of Google BigQuery as its SQL dialect.
The toy dataset used all through this publish is a desk of orders and a desk 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 |
prospects
| customer_id | is_premium_customer | identify…
