Polars: The Rust DataFrame Library That Makes Pandas Look Slow

Polars is a blazing-fast DataFrame library built in Rust that outperforms Pandas by 10-100x on large datasets, with lazy evaluation and parallel execution built in.

Mahmudul Haque Qudrati

CEO & ML Engineer

March 7, 2026

7 min read

// tags

#polars#pandas#dataframe#rust#performance

FIG. ART-21

7 min read

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Polars: The Rust DataFrame Library That Makes Pandas Look Slow

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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.

Polars: The Rust DataFrame Library That Makes Pandas Look Slow

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