One-Minute Guide To Becoming a Polars-savvy Data Scientist

Pandas is an essential library in almost all Data Science projects.

But it has many limitations.

For instance, Pandas:

• always adheres to single-core computation

• offers no lazy execution

• creates bulky DataFrames

• is slow on large datasets, and many more

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

It provides two APIs:

• Eager: Executed instantly, like Pandas.

• Lazy: Executed only when one needs the results.

The visual presents the syntax comparison of Polars and Pandas for various operations.

It is clear that Polars API is extremely similar to Pandas'.

Thus, contrary to common belief, the transition from Pandas to Polars is not that intimidating and tedious.

If you know Pandas, you (mostly) know Polars.

In most cases, the transition will require minimal code updates.

But you get to experience immense speed-ups, which you don't get with Pandas.

I recently did a comprehensive benchmarking of Pandas and Polars, which you can read here: Pandas vs Polars — Run-time and Memory Comparison.

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

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.