pandas vs Polars

pandas has been Python's standard DataFrame library for over a decade: nearly every data tutorial, plotting library, and machine-learning tool accepts its DataFrames. Polars is a newer DataFrame library written in Rust on Apache Arrow's columnar memory format. It runs queries across all CPU cores, can optimize whole pipelines before executing them, and often processes data many times faster with much less memory.

Short verdict: use Polars for new data pipelines, large files, and performance-sensitive transformations; keep pandas where its ecosystem matters — exploratory notebooks with many pandas-based tools, libraries that require pandas, and existing codebases that work. The two interoperate cheaply through Arrow, so you can adopt Polars one pipeline at a time instead of rewriting everything.

TL;DR

Quick Example

Revenue per country from paid orders. pandas:

Polars, lazily — the filter and the four needed columns are pushed down into the Parquet scan:

The pandas version reads every column of every row into memory before filtering. The Polars version builds a query plan, reads only what it needs, and runs the aggregation in parallel.

Core Concepts

Head-to-Head

Expressions

Polars code is built from expressions: pl.col("price") * 1.2, pl.col("name").str.to_lowercase(), pl.col("amount").sum().over("customer_id"). Expressions inside select, with_columns, filter, and agg run in parallel and are optimized together. Row-wise Python functions (map_elements) exist but discard most of the speed — the same lesson as avoiding apply in pandas.

Lazy Execution

pl.scan_* functions and .lazy() create a LazyFrame: operations build a plan that Polars optimizes — predicate and projection pushdown, common subexpression elimination, join reordering — before .collect() runs it. .explain() shows the optimized plan. Lazy queries can also run on the streaming engine to process data larger than RAM.

Types and Missing Data

Polars is strict: a column has one type, and mixing types raises an error rather than silently producing an object column. Missing values are null for every type, so integer columns stay integers when values are missing — in pandas, they historically became floats unless you used nullable dtypes. Recent pandas versions narrow the gap with Arrow-backed dtypes and copy-on-write semantics.

The Index

pandas aligns operations on the index, which is powerful for time series and labeled data but a frequent source of surprises after filters, merges, and groupby. Polars has no index; joins and alignment are always explicit, and group_by returns ordinary columns.

Ecosystem and Interoperability

Adopting Polars Incrementally

  1. Start with batch pipelines that read large files and run joins or aggregations — the biggest wins.
  2. Use lazy scans (scan_parquet, scan_csv) from the start.
  3. Convert to pandas at boundaries where a library requires it.
  4. Translate idioms: boolean indexing → filter, assign → with_columns, groupby().agg() → group_by().agg(), merge → join, apply → expressions.
  5. Compare results on real data, paying attention to nulls, types, and sort order.

Best Practices

Think in Columns, Not Rows

In both libraries, vectorized column operations are fast and row-wise Python is slow. Polars rewards this more strongly: express logic as expressions.

Default to Lazy for Files

Scanning lazily lets Polars skip unneeded columns and rows, which often matters more than raw compute speed.

Keep pandas Where It Shines

Interactive exploration with pandas-based tools, statistical libraries, and established code are good reasons to stay. Use the right tool per job rather than standardizing dogmatically.

Measure on Your Data

Benchmarks vary with data shape and operations. Time your actual pipelines before and after switching. See pandas Performance.

Common Mistakes

Writing pandas Code in Polars

Collecting Too Early

Assuming Identical Null Semantics

FAQ

Is Polars faster than pandas?

Usually, often substantially, for joins, group-bys, filters, and file reads, thanks to multi-threading, Arrow memory, and query optimization. For small DataFrames the difference may not matter.

Should I replace pandas with Polars?

For new pipelines and performance-sensitive work, Polars is a strong default. Keep pandas where libraries require it or code already works well; the two convert cheaply.

Does Polars work with scikit-learn?

Yes. Many scikit-learn estimators accept Polars input, and transformers can output Polars DataFrames. Some libraries still require pandas or NumPy, in which case you convert at the boundary.

Can Polars handle data larger than memory?

Lazy queries can run on Polars' streaming engine, processing data in batches. For very large or distributed workloads, Spark or a query engine such as DuckDB or Trino may still be more appropriate.

Why does Polars not have an index?

By design: explicit columns make joins and alignment predictable and parallel execution simpler. Operations that used the index in pandas use explicit columns and joins in Polars.

Related Topics

References