Polars vs Pandas in 2026: Should You Switch?

Polars is a DataFrame library written in Rust that processes columns in parallel using the Apache Arrow memory format, while pandas is the long-established Python DataFrame library built on NumPy. In 2026, the practical question is no longer which one is faster in a toy test, but which one earns its place in each stage of your pipeline.

Key takeaways

• On our own 5-million-row benchmark, Polars beat pandas by 4.5x on CSV reading, 7x on group-by, and 8.7x on joins.
• Small, simple operations show a much smaller gap: filter plus sort was only 1.5x faster in Polars.
• Polars wins through multithreading, Arrow memory layout, and a lazy query optimiser, not through a single trick.
• Pandas still wins at the machine learning boundary, where scikit-learn, plotting, and Excel tooling live.
• The sensible 2026 answer for most teams is a hybrid: Polars for ETL, pandas where the ecosystem demands it.

How much faster is Polars, really?

Vendor benchmarks deserve scepticism, so we ran our own. The test machine was a modest 2-core virtual machine with 7 GB of RAM, running pandas 3.0.6 and Polars 2.1.0 on Python 3.12. The dataset was 5 million synthetic rows with 200 categories, a float column, and an integer column. Each timing below is the median of three runs.

Operation (5M rows)pandasPolarsSpeedupRead CSV1.93 s0.43 s4.5xGroup-by sum0.49 s0.07 s7.0xJoin (5M x 200 rows)0.78 s0.09 s8.7xFilter plus sort0.12 s0.08 s1.5x

The pattern matters more than any single number. Polars pulls away on the expensive, multi-step operations (reads, aggregations, joins) and barely leads on cheap ones. That matches independent third-party results: the Uvik Software 2026 DataFrame benchmark, run on a 2-core machine with 8 GB of RAM using pandas 3.0.6 and Polars 2.0.0, found Polars 5.7x faster across ten group-by queries on 10 million rows and 3.75x faster on five join queries. Under a 5.84 GiB memory cap at 50 million rows, pandas ran out of memory while loading, while Polars finished 9 of 10 queries.

What we could not verify: the Polars team's own PDS-H numbers, which claim a 94x gap on the full decision-support suite against pandas 2.2.3. That is a vendor claim on vendor-chosen hardware, and we treat it as an upper bound, not a promise. Your numbers will differ with data shape, column cardinality, and core count, so benchmark on your own data before committing.

What actually makes Polars faster?

Three design choices do most of the work. First, Polars is multithreaded by default: a group-by fans out across all cores without you asking. Pandas runs most operations on a single thread. Second, Polars stores data in Arrow format, so columns are contiguous, typed, and cheap to scan, while pandas still carries the overhead of its row index and per-column Python objects in its default dtypes. Third, Polars has a lazy API that optimises the whole query before running it: filters and column selections get pushed down to the file reader, so work that produces no output never happens.

import polars as pl

# Lazy: nothing runs until .collect(), and the optimiser
# prunes the filter and the unused columns first.
result = (
    pl.scan_csv("sales.csv")
    .filter(pl.col("qty") > 10)
    .group_by("category")
    .agg(pl.col("value").mean().alias("avg_value"))
    .collect()
)

On our machine, the lazy version of a filter plus group-by ran in 0.06 seconds against 0.07 seconds eager, a small win here, but on wide files with many unused columns the pushdown effect is where the large savings come from. The honest caveat: lazy mode only helps if you actually chain operations. A single eager call on a small frame gains almost nothing.

Where does pandas still win?

Speed is not the whole job. Pandas wins wherever the surrounding ecosystem matters more than raw throughput. Scikit-learn, statsmodels, matplotlib, and seaborn all expect pandas objects or NumPy arrays, and pandas reads and writes Excel files that Polars does not handle natively. For datasets under roughly 100,000 rows, the absolute time difference is milliseconds, and rewriting working code for milliseconds is a bad trade. Team familiarity counts too: almost every data tutorial, Stack Overflow answer, and legacy codebase speaks pandas, and some APIs, such as pivot tables with multiple margins, still have no direct Polars equivalent.

Pandas 3, released in 2026, also narrowed the gap for existing code. Copy-on-Write is now the default behaviour, which removes a whole class of silent bugs and wasted copies, and the optional Arrow-backed dtypes speed up string-heavy work. Pandas 3 requires Python 3.11 or newer and drops several long-deprecated features, so upgrading a legacy codebase still needs a test pass, but it is no longer the obvious laggard it was two years ago.

Polars has its own traps. Row-wise Python functions applied with map_elements run one row at a time in Python and throw away the entire speed advantage; almost every such case has a native vectorised expression, such as str.to_uppercase(), and you should reach for that first. Plotting is another gap: Polars has no built-in plotting, so visualisation still means converting to pandas or feeding a plotting library directly. And Polars' strict typing is a feature until it surprises you: a column that mixes integers and nulls behaves differently than the same column in pandas, so check dtypes explicitly after every read.

How do you switch without rewriting everything?

Do not rewrite your whole codebase in one go. Migrate the hot path first, usually the ETL stage, and keep pandas at the boundary where the ecosystem needs it. The two APIs are close enough that the translation is mostly mechanical:

# pandas
df.groupby("category")[["value", "qty"]].sum()

# Polars
df.group_by("category").agg(
    pl.col("value").sum(),
    pl.col("qty").sum(),
)

Conversion between the two is one call each way, but watch one sharp edge we hit ourselves while benchmarking: converting a pandas frame to Polars with pl.from_pandas() requires PyArrow installed unless every column is plain NumPy-backed. Pandas 3 defaults string columns to its new string dtype, which is not NumPy-backed, so install PyArrow or build the Polars frame directly from your arrays. At the machine learning boundary, convert once, not per step:

# Polars for the heavy lifting, pandas only where sklearn needs it
features = pl.scan_parquet("features.parquet").filter(...).collect()
model.fit(features.to_pandas(), labels)  # one conversion, at the edge

After migrating a step, verify it: compare row counts, check a few grouped totals against the old pandas output, and use Polars' equals check or pandas' testing helpers on a sample. Silent dtype changes, especially around nulls and timestamps, are the most common migration bug, not speed regressions.

So, should you switch in 2026?

Use this decision rule. If your frames regularly exceed a million rows, or the same ETL job runs on a schedule, switch that pipeline to Polars: the 4x to 9x gains we measured are real, and they compound into lower compute bills. If you are starting a greenfield data pipeline, start in Polars and convert at the edges. If your existing pandas code is stable, under 100,000 rows, and wrapped in notebooks and sklearn models, leave it alone: the migration cost buys you milliseconds. And if your team is learning data work for the first time, learn pandas first for the ecosystem, then add Polars as the performance tool. The two libraries are not a war. In 2026, the strongest Python data stack uses both, each where it is actually best.

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