Polars: The Rust DataFrame Library That Makes Pandas Look Slow

Why Polars Is Taking Over Data Science

If you work with data in Python, you have almost certainly felt the pain of waiting for Pandas to process a large file. Polars is the library that fixes this. Built in Rust and exposed to Python, it consistently benchmarks 10-100x faster than Pandas on real-world workloads.

The DuckDB benchmark suite shows Polars outperforming Pandas on groupby and join operations across dataset sizes from 500MB to 50GB. The gap widens as data grows.

Why Polars Is So Fast

Three architectural decisions drive Polars performance:

Parallel execution by default. Every Polars operation uses all CPU cores automatically. Pandas is single-threaded by design.

Lazy evaluation with query optimization. When you use pl.LazyFrame, Polars builds a query plan and optimizes it before executing - pushing down filters, reordering joins, and eliminating unnecessary columns.

SIMD and cache-friendly memory layout. Polars uses Apache Arrow memory format internally, which is designed for vectorized CPU instructions.

Polars vs Pandas Syntax

Pandas:

import pandas as pd

df = pd.read_csv("sales.csv")
result = df[df["region"] == "EU"].groupby("product")["revenue"].sum().reset_index()

Polars:

import polars as pl

result = (
    pl.read_csv("sales.csv")
    .filter(pl.col("region") == "EU")
    .group_by("product")
    .agg(pl.col("revenue").sum())
)

The expressions API is more composable and avoids the index complexity that makes Pandas code hard to read.

LazyFrame for Query Optimization

The real power comes from pl.LazyFrame:

import polars as pl

result = (
    pl.scan_parquet("data/*.parquet")  # lazy  -  nothing loaded yet
    .filter(pl.col("date") >= "2025-01-01")
    .select(["date", "user_id", "revenue"])
    .group_by("date")
    .agg(pl.col("revenue").sum())
    .sort("date")
    .collect()  # execute now
)

scan_parquet() enables out-of-core processing - you can query files larger than RAM because only the needed rows and columns are loaded.

Reading Multiple Formats

import polars as pl

# CSV
df = pl.read_csv("data.csv", infer_schema_length=10000)

# Parquet (columnar  -  much faster than CSV)
df = pl.read_parquet("data.parquet")

# JSON (newline-delimited)
df = pl.read_ndjson("data.jsonl")

# Multiple Parquet files at once
df = pl.scan_parquet("data/year=2025/*.parquet").collect()

Migrating from Pandas

The migration is not zero-cost but is worth it for large datasets. Key differences:

• No row index - use explicit columns
• df["col"] returns a Series, not a scalar - use .item() for single values
• inplace=True does not exist - all operations return new DataFrames
• String methods: pl.col("name").str.to_uppercase() instead of .str.upper()

For a hybrid migration, Polars and Pandas interoperate:

# Polars → Pandas
pandas_df = polars_df.to_pandas()

# Pandas → Polars
polars_df = pl.from_pandas(pandas_df)

When to Use Polars vs Pandas

Use Polars when: datasets exceed 1M rows, you need parallel processing, or you are building pipelines. Use Pandas when: you need deep ecosystem compatibility (Matplotlib, old scikit-learn pipelines), small interactive data exploration, or existing Pandas-heavy codebases.

Resources: Polars docs, GitHub, benchmark.