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If you’ve worked with large datasets in Python, you’ve probably faced slowdowns with pandas. Enter Polars: a modern DataFrame library built in Rust for speed and efficiency. Polars offers familiar DataFrame syntax (in Python, R, even SQL) but under the hood it’s optimized with a multi-threaded query engine. According to the Polars docs, it’s “written from scratch in Rust” and designed to split work across all CPU cores. The result? Operations on Polars can be 5–10× faster than pandas on the same data, and in many cases 10–100× faster.

Why Polars Is So Fast

Several design choices make Polars lightning fast:

  • Rust Core & Parallelism: The engine is in Rust, which means low-level optimizations. It automatically uses all CPU cores without you having to manage threads.

  • Lazy Execution: Like Spark DataFrames, Polars can build a query plan and optimize it before running. This means it can fuse operations and avoid unnecessary work under the hood.

  • Out-of-Core Streaming: Polars can process data that doesn’t fit in memory via its streaming API.

  • Arrow Integration: Built on Apache Arrow, Polars stores columns in contiguous memory for high performance and zero-copy interoperability.

  • Optional GPU Support: For pure in-memory jobs, Polars can even use NVIDIA GPUs to accelerate queries (useful for specialized cases).

The net result is impressive. In benchmarks, Polars can use much less memory too: where pandas might need 5–10× the data size in RAM, Polars often only needs 2–4× 🤯. And since it’s single-node, you don’t pay the overhead of cluster coordination like Spark or Dask.

Getting Started with Polars

Using Polars is straightforward for Python users. Install it with:

pip install polars

Then import and use it similarly to pandas. Here are some quick examples:

import polars as pl
# Create a Polars DataFrame (similar to pandas) 
df = pl.DataFrame({"age": [25, 32, 47], "name": ["Alice", "Bob", "Charlie"] })

You can now filter, add columns, group, and aggregate with concise syntax. For example, to compute a new column:

df = df.with_column((pl.col("age") * 2).alias("double_age"))

Or to group by and aggregate:

df.groupby("name").agg(pl.col("age").mean())

You can also use lazy evaluation for performance on complex chains of operations:

df = pl.read_csv("big_file.csv").lazy()

result = (
    df.filter(pl.col("price") > 100)
      .groupby("category")
      .agg(pl.col("price").mean().alias("avg_price"))
      .sort("avg_price", descending=True)
)

result.collect()

In lazy mode, nothing runs until .collect() is called — and then Polars optimizes the whole plan.

TL;DR

  • Polars is a blazing-fast DataFrame library, written in Rust and built for performance.

  • Automatically parallelized, memory-efficient, and optionally out-of-core (works with large datasets).

  • Supports eager and lazy APIs. Lazy mode gives Spark-like optimization with simple syntax.

  • Integrates with Arrow and optionally NVIDIA GPUs.

  • Faster and more memory-efficient than pandas for large-scale data work.

Learn more at the Polars Book or check out the GitHub repo.

Happy Coding!