Become a Trilingual Data Scientist with These 15 Pandas ↔ Polars ↔ SQL Translations

SQL and Pandas are powerful tools for data scientists to work with data.

Thus, proficiency in both frameworks is extremely valuable to data scientists.

But lately, Polars has also gained much popularity among data scientists.

Polars is a lightning-fast DataFrame library that addresses these limitations.

This is because it addresses many of Pandas’ limitations, such as:

• Pandas always adheres to single-core computation → Polars is multi-core.

• Pandas offers no lazy execution → Polars does.

• Pandas creates bulky DataFrames → Polars’ DFs are lightweight.

• Pandas is slow on large datasets → Polars is remarkably efficient.

The visual below depicts the 15 most common tabular operations in Pandas and their corresponding translations in SQL and Polars.

It will help you build proficiency in all three frameworks.

👉 Over to you: What are some other faster alternatives to Pandas that you are aware of?

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