Polars: A Faster DataFrame

Polars is a Rust-backed DataFrame library built on Apache Arrow. It is multi-threaded, columnar, and frequently several times faster than pandas, with both an eager API for quick work and a lazy API that optimizes whole queries.

Learn Polars: A Faster DataFrame in our free Pandas course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick…

Part of the free Pandas course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

Learn the eager vs lazy distinction, write expressions with pl.col, scan files with scan_csv and run them with collect, and understand why Arrow memory makes Polars so quick.

Polars is written in Rust , stores columns in the Apache Arrow columnar format, and runs operations across multiple CPU cores . Pandas, by contrast, is mostly single-threaded. The result is that the same groupby or join often runs several times faster in Polars.

The runnable example below uses plain pandas to compute a per-region total — the same result Polars would produce with its own expression syntax.

In the eager API, operations run immediately, like pandas. The key new idea is the expression : you describe what you want with pl.col("name") rather than indexing the DataFrame directly. Methods like select , filter , and group_by take expressions.

The lazy API is where Polars really shines. pl.scan_csv returns a LazyFrame and reads nothing yet; it just builds a query plan. Polars then optimizes the whole plan — pushing filters and column selection down to the read step — and runs it only when you call .collect() .

Reproduce the Polars result in pandas. Fill in the blank to total the amount per region. Expected output: North 270, South 170.

Lesson complete — you can reach for a faster engine!

You now know why Polars is fast (Rust + Arrow + multi-threading), the difference between the eager and lazy APIs, how to write expressions with pl.col, how scan_csv and collect work, and how to move data to and from pandas.

🚀 Up next: Pandas & Spark — distributed big-data processing with PySpark.

Practice quiz

What language is the Polars engine written in?

  • Rust
  • Pure Python
  • Java
  • C# only

Answer: Rust. Polars is implemented in Rust, which is a big part of why it is fast and memory efficient.

Which in-memory format does Polars use for its columns?

  • JSON
  • Pickle
  • Apache Arrow
  • Plain Python lists

Answer: Apache Arrow. Polars stores data in the Apache Arrow columnar format for speed and zero-copy interop.

Which two API styles does Polars offer?

  • Sync and async
  • Eager and lazy
  • Local and remote
  • Typed and untyped

Answer: Eager and lazy. Polars has an eager API that runs immediately and a lazy API that optimizes a query plan.

How do you reference a column inside a Polars expression?

  • col_ref('name')
  • pl.column()
  • df.get()
  • pl.col('name')

Answer: pl.col('name'). pl.col('name') builds an expression that refers to a column for select, filter, and agg.

Which function starts a LAZY query over a CSV without reading it all yet?

  • pl.scan_csv
  • pl.read_csv
  • pl.open_csv
  • pl.load_csv

Answer: pl.scan_csv. pl.scan_csv returns a LazyFrame and only reads what the optimized plan needs.

What method executes a Polars LazyFrame and returns a DataFrame?

  • .run()
  • .collect()
  • .execute()
  • .compute()

Answer: .collect(). Calling .collect() runs the optimized lazy query plan and materializes a DataFrame.

Why is the lazy API often faster than eager?

  • It uses the GPU
  • It caches to disk
  • It skips all computation
  • It optimizes the whole query plan, e.g. predicate and projection pushdown

Answer: It optimizes the whole query plan, e.g. predicate and projection pushdown. The lazy engine sees the full plan and applies optimizations like pushing filters down early.

How does Polars typically compare to pandas on large operations?

  • Always slower
  • About the same
  • Often significantly faster and multi-threaded
  • Cannot do groupby

Answer: Often significantly faster and multi-threaded. Polars is multi-threaded and columnar, so it is frequently much faster than single-threaded pandas.

Which Polars call selects columns using expressions?

  • df.take('a','b')
  • df.select(pl.col('a'), pl.col('b'))
  • df.pick('a','b')
  • df.columns('a','b')

Answer: df.select(pl.col('a'), pl.col('b')). df.select(...) takes expressions like pl.col('a') to build the output columns.

How do you convert a Polars DataFrame to pandas when you need it?

  • df.to_pandas()
  • df.pandas()
  • pd(df)
  • df.as_pandas()

Answer: df.to_pandas(). df.to_pandas() converts a Polars DataFrame into a pandas DataFrame, often zero-copy via Arrow.