Polars vs Pandas in 2026: When to Switch and What to Expect

A data pipeline that used to take 40 seconds on a mid-size dataset now finishes in under 4. No infrastructure changes. No cloud upgrades. Just swapping pandas for Polars.

That kind of before-and-after is common enough now that the question has shifted from “should we look at Polars?” to “which pipelines are worth migrating first?”

Polars released 1.0 in June 2024, signaling API stability. The ecosystem around it has grown since: connectors, integrations, cloud support. It’s not experimental. Teams are running it in production on datasets that would have required Spark a few years ago.

This is a practical look at the difference between the two libraries, where each one fits, and what migration actually involves.

Why Polars Is Faster

The performance gap has three main sources.

Arrow memory layout. Polars stores data in the Apache Arrow columnar format. Operations on entire columns are faster than row-by-row access because the data is contiguous in memory. Pandas 2.0 added optional Arrow-backed dtypes, but it’s not the default.

Multi-threading. Polars uses multiple CPU cores by default. A groupby or filter on a large DataFrame uses all available cores without any configuration. Pandas is single-threaded for most operations.

Lazy evaluation. Polars has a LazyFrame API that builds a query plan instead of executing immediately. Before running, the query optimizer can push down filters (apply the filter before the join, not after), eliminate unused columns, and reorder operations. You get better performance just by writing scan_csv instead of read_csv.

None of these require you to know Rust or configure a cluster. They’re defaults.

The API Difference

Pandas and Polars share the same mental model (DataFrames with column operations), but the syntax is different enough that you can’t drop Polars in without changes.

Selection:

# pandas
df[["name", "price"]]
df.loc[df["price"] > 100, ["name", "price"]]

# polars
df.select(["name", "price"])
df.filter(pl.col("price") > 100).select(["name", "price"])

Groupby:

# pandas
df.groupby("category")["price"].agg(["mean", "sum"])

# polars
df.group_by("category").agg([
    pl.col("price").mean().alias("price_mean"),
    pl.col("price").sum().alias("price_sum"),
])

Apply / map:

This is where the difference matters most. In pandas, .apply() calls a Python function row-by-row. It’s slow, but flexible. Polars strongly discourages .map_elements() (its equivalent) and instead provides a large library of built-in expressions that run at native speed.

# pandas — calling Python on every row
df["discount"] = df.apply(lambda row: row["price"] * 0.1 if row["is_member"] else 0, axis=1)

# polars — using conditional expression, runs natively
df = df.with_columns(
    pl.when(pl.col("is_member"))
    .then(pl.col("price") * 0.1)
    .otherwise(0)
    .alias("discount")
)

The Polars version runs faster and uses less memory. The tradeoff: you need to learn the expression API, which has a learning curve.

Lazy Evaluation in Practice

The LazyFrame API is where Polars really separates from pandas. You describe the transformations you want; Polars figures out the optimal order to run them.

import polars as pl

# Use scan_csv (lazy) instead of read_csv (eager)
result = (
    pl.scan_csv("orders.csv")
    .filter(pl.col("status") == "completed")
    .filter(pl.col("amount") > 100)
    .group_by("customer_id")
    .agg([
        pl.col("amount").sum().alias("total_spend"),
        pl.col("order_id").count().alias("order_count"),
    ])
    .sort("total_spend", descending=True)
    .limit(1000)
    .collect()  # Execute the query plan
)

If the CSV has 10 million rows but only 50,000 match the filters, Polars won’t load the rest into memory. The optimizer pushes the filters as early as possible in the execution.

You can inspect the query plan before running it:

lazy_df = pl.scan_csv("orders.csv").filter(pl.col("amount") > 100)
print(lazy_df.explain())

This is useful for debugging unexpectedly slow queries.

Where Pandas Still Wins

Ecosystem compatibility. Scikit-learn, many ML libraries, and most data tools expect pandas DataFrames. You can convert between them (df.to_pandas() / pl.from_pandas(df)), but if your pipeline feeds directly into sklearn estimators, pandas avoids that conversion overhead.

Row-level Python operations. If you genuinely need to apply custom Python logic per row and can’t express it as a Polars expression, pandas apply is available and familiar. Polars’ equivalent exists but the library’s design philosophy pushes you toward expressions first.

Time series tooling. Pandas has years of investment in time series resampling, offset aliases, and business calendar support. Polars has improved here but doesn’t match the breadth of pd.tseries.

Institutional familiarity. If your team knows pandas and the dataset fits in memory without performance problems, migrating has a cost. That cost needs a concrete payoff to justify it.

What Migration Actually Looks Like

For a typical ETL script, plan on 1-3 hours per file, depending on complexity and how heavily .apply() is used.

The conversion checklist:

1. Replace pd.read_csv with pl.read_csv (eager) or pl.scan_csv (lazy, preferred for large files)
2. Replace df.rename(columns={...}) with df.rename({...})
3. Replace df.apply(fn) with Polars expressions (this takes the most thought)
4. Replace df.loc[condition] with df.filter(condition)
5. Replace chained assignments (df["col"] = ...) with df.with_columns(...)
6. Replace df.groupby(...).apply(fn) with df.group_by(...).agg(...)

A utility that helps when you’re stuck:

# If you really need a Python function per-group and can't rewrite it:
result = df.group_by("category").map_groups(
    lambda group_df: my_python_function(group_df)
)

This is slower than native expressions but faster than pandas row-level apply in most cases.

Type System

Polars is stricter about types. Pandas often coerces silently. An integer column with one null becomes a float. Polars has first-class null support without type coercion. If a column has mixed types in your CSV, Polars will raise an error where pandas might have quietly converted.

This strictness is a feature. It catches data quality issues early. But it means migration sometimes surfaces bugs in data assumptions that pandas was hiding.

# Polars will error if you try to do arithmetic on a string column
# pandas often silently drops or coerces

# Explicit type casting in Polars
df = df.with_columns(
    pl.col("price").cast(pl.Float64)
)

Pandas 2.x With Arrow Backends

Pandas 2.0 added Arrow-backed dtypes as an option. You can get some of Polars’ memory benefits without switching libraries:

import pandas as pd

df = pd.read_csv("data.csv", dtype_backend="pyarrow")

This helps with memory usage and some operations are faster. But it doesn’t give you lazy evaluation, automatic multi-threading, or the expression optimizer. It’s a migration path option if you want incremental improvement, not a full replacement.

Practical Guidance

If you’re starting a new project and don’t have ML library integration constraints, default to Polars. The API is cleaner for complex transformations, and the performance defaults are better.

If you have an existing pandas codebase: identify the bottleneck files first. Profiling will show whether the slow parts are actually pandas operations or something else (I/O, network, Python logic). Migrate only the files where pandas is the actual bottleneck.

For data that fits in memory and finishes in under a second, the choice doesn’t matter much. Pick whichever your team knows.

For datasets above 1GB, batch pipelines, or anything that’s currently slow, Polars is worth the migration effort. The performance difference is real and the API, once learned, is cleaner for complex transformations than chained pandas operations.