Polars vs Pandas 2026: Benchmarks That Actually Matter

Why Teams Keep Switching (and Why Some Don’t)

Polars hit 1.0 in mid-2024. By early 2026, half the data teams I talk to have either migrated or are actively testing it. The other half? Still on Pandas, and not because they’re unaware of the hype.

The switch isn’t about raw speed anymore. Polars is faster—everyone knows that. The question is whether the migration cost is worth it for your workload, and whether you’re hitting the specific bottlenecks Polars solves.

I’ve run both in production for the past year. Here’s what the benchmarks don’t tell you, and what actually matters when you’re deciding whether to migrate.

The Lazy Execution Trap Nobody Warns You About

Polars uses lazy evaluation by default. You build a query plan, then call .collect() to execute it. In theory, this lets Polars optimize the entire pipeline before running anything.

In practice? You’ll write code like this and wonder why it’s slow:

import polars as pl

# Load 10GB parquet file
df = pl.scan_parquet("transactions.parquet")

# Filter, group, aggregate
result = (
    df.filter(pl.col("amount") > 100)
    .group_by("customer_id")
    .agg(pl.col("amount").sum())
    .collect()  # Execute here
)

print(result.shape)  # Fast

# Now you want to explore the top 10 customers
top10 = result.sort("amount", descending=True).head(10)
print(top10)  # Also fast, result is already in memory

# But what if you want to re-filter the original data?
high_value = (
    df.filter(pl.col("amount") > 1000)  # Starts from scan again
    .group_by("customer_id")
    .agg(pl.col("amount").mean())
    .collect()
)

That second query re-scans the entire parquet file. You’re not reusing the filtered DataFrame—you’re rebuilding from the lazy scan. With Pandas, you’d have loaded the data once into memory and worked from there.

The fix? Either .collect() intermediate results you’ll reuse, or redesign your pipeline to do everything in one lazy chain. Neither is wrong, but it’s a mental shift. Pandas is eager by default—you see the data at every step. Polars hides execution until you ask for it.

Memory: Where Polars Actually Wins

Pandas loads everything into RAM. A 5GB CSV becomes a 5GB+ DataFrame (often more due to object dtype overhead). If you’re on an 8GB laptop, you’re toast.

Polars scans from disk. You can process files larger than RAM by streaming through them, filtering early, and only collecting the results you need.

Here’s a real example from a fraud detection pipeline I worked on:

# Pandas approach: load everything, then filter
import pandas as pd

df = pd.read_parquet("clickstream.parquet")  # 12GB file, 16GB DataFrame in memory
suspicious = df[df["click_velocity"] > 10]
fraud_users = suspicious.groupby("user_id")["amount"].sum()

# Result: MemoryError on 16GB machine

# Polars approach: filter during scan
import polars as pl

fraud_users = (
    pl.scan_parquet("clickstream.parquet")
    .filter(pl.col("click_velocity") > 10)
    .group_by("user_id")
    .agg(pl.col("amount").sum())
    .collect()
)

# Result: 2GB peak memory, runs fine

The Polars query never loads the full 12GB. It reads chunks, applies the filter, aggregates, and discards what it doesn’t need. Peak memory: 2GB. The same operation in Pandas required 16GB+ and crashed.

This isn’t a microbenchmark. It’s the difference between “runs on my laptop” and “requires a 32GB cloud instance.”

String Operations: Pandas Still Has the Edge

Polars is fast for numeric operations. But if you’re doing heavy string manipulation—regex, complex parsing, categorical encoding—Pandas is often faster in practice (as of Polars 1.15, early 2026).

Here’s a test on a DataFrame with 10 million user agent strings:

import pandas as pd
import polars as pl
import time

# Generate sample data
user_agents = ["Mozilla/5.0 (Windows NT 10.0; Win64; x64)..."] * 10_000_000

# Pandas: extract browser version via regex
start = time.time()
df_pd = pd.DataFrame({"ua": user_agents})
df_pd["version"] = df_pd["ua"].str.extract(r"Chrome/(\d+\.\d+)")
print(f"Pandas: {time.time() - start:.2f}s")  # ~8.2s on M1 MacBook

# Polars: same operation
start = time.time()
df_pl = pl.DataFrame({"ua": user_agents})
df_pl = df_pl.with_columns(
    pl.col("ua").str.extract(r"Chrome/(\d+\.\d+)", 1).alias("version")
)
print(f"Polars: {time.time() - start:.2f}s")  # ~11.3s

Polars was 40% slower. Why? Pandas uses NumPy’s optimized C extensions for string operations, while Polars uses Rust’s regex crate, which is fast but not always faster than decades of NumPy optimization.

If your pipeline is mostly .str.replace(), .str.split(), and .str.contains(), test both. Don’t assume Polars wins by default.

Joins: Where Polars Destroys Pandas

Large joins are where Polars shines. Pandas uses a nested loop merge for most cases. Polars uses a hash join with parallel execution.

Benchmark: joining two DataFrames (10M rows each) on a single key.

import pandas as pd
import polars as pl
import numpy as np
import time

# Create test data
np.random.seed(42)
df1_data = {"id": np.arange(10_000_000), "value_a": np.random.rand(10_000_000)}
df2_data = {"id": np.arange(10_000_000), "value_b": np.random.rand(10_000_000)}

# Pandas join
df1_pd = pd.DataFrame(df1_data)
df2_pd = pd.DataFrame(df2_data)
start = time.time()
result_pd = df1_pd.merge(df2_pd, on="id", how="inner")
print(f"Pandas: {time.time() - start:.2f}s")  # ~18.7s

# Polars join
df1_pl = pl.DataFrame(df1_data)
df2_pl = pl.DataFrame(df2_data)
start = time.time()
result_pl = df1_pl.join(df2_pl, on="id", how="inner")
print(f"Polars: {time.time() - start:.2f}s")  # ~2.1s

Polars was 9x faster. The gap widens with multiple join keys or when one table is much smaller (Polars auto-optimizes broadcast joins).

But here’s the catch: if you’re joining on non-unique keys and need to preserve row order, Pandas and Polars behave differently. Pandas maintains insertion order; Polars does not (by design, for speed). If your downstream code assumes sorted output, you’ll need an explicit .sort() in Polars.

Expression API vs Method Chaining

Pandas uses method chaining:

(
    df.groupby("category")
    .agg({"sales": "sum", "units": "mean"})
    .reset_index()
    .sort_values("sales", ascending=False)
)

Polars uses an expression-based API:

(
    df.group_by("category")
    .agg([
        pl.col("sales").sum(),
        pl.col("units").mean()
    ])
    .sort("sales", descending=True)
)

The Polars version is more explicit—you name every column operation. This feels verbose at first, but it’s easier to reason about. You know exactly which columns are being touched, and the query optimizer can see the full dependency graph.

Pandas lets you do .agg({"col": func}), which is concise but hides what’s happening. You can’t tell at a glance whether the aggregation creates new columns or modifies existing ones.

After a month, I preferred the Polars style. After three months, I missed Pandas’s brevity. It’s a trade-off.

The Schema Strictness Problem

Polars enforces schema consistency. If you try to append a DataFrame with a different column order or dtype, it errors. Pandas silently coerces or reorders.

Example:

import pandas as pd
import polars as pl

# Pandas: silent coercion
df1 = pd.DataFrame({"a": [1, 2], "b": [3.0, 4.0]})
df2 = pd.DataFrame({"a": [5, 6], "b": [7, 8]})  # b is int, not float
result = pd.concat([df1, df2])
print(result.dtypes)  # a: int64, b: float64 (coerced)

# Polars: explicit error
df1 = pl.DataFrame({"a": [1, 2], "b": [3.0, 4.0]})
df2 = pl.DataFrame({"a": [5, 6], "b": [7, 8]})
try:
    result = pl.concat([df1, df2])
except Exception as e:
    print(e)  # SchemaError: column 'b' has dtype i64 but expected f64

This is good for production pipelines—you want to know when your schema changes unexpectedly. But it’s annoying in exploratory work. You’ll spend time adding .cast() calls that Pandas would’ve handled silently.

When to Stay on Pandas

1. Your data fits in RAM comfortably. If you’re working with <1GB DataFrames, Pandas is fast enough. The performance gap only matters at scale.

2. You rely on the ecosystem. Libraries like scikit-learn, statsmodels, and seaborn expect Pandas DataFrames. Polars can convert (.to_pandas()), but you’re adding an extra step.

3. You need compatibility. If you’re shipping code to clients or open-source users, Pandas is still the default. Requiring Polars adds a dependency barrier.

4. String-heavy workloads. As shown above, Pandas is often faster for complex string operations.

5. You’re new to data analysis. Pandas has 15 years of Stack Overflow answers. Polars is improving, but the community is smaller.

When to Switch to Polars

1. You’re hitting memory limits. If you’re upgrading EC2 instances just to load data, Polars pays for itself immediately.

2. You do a lot of joins. The performance gap is massive.

3. You’re processing parquet/arrow files. Polars reads them natively and faster than Pandas (which uses PyArrow under the hood).

4. You want better error messages. Polars errors are clearer and include the expression that failed. Pandas errors are often cryptic.

5. You’re building a new pipeline from scratch. No migration cost, just pick the faster tool.

Migration Strategy That Worked for Me

Don’t rewrite everything at once. Start with one bottleneck:

1. Profile your pipeline. Use cProfile or py-spy to find the slowest part. If it’s a join or a large aggregation, try Polars there first.

2. Convert at the boundaries. Load data with Polars, process it, then .to_pandas() before passing to scikit-learn. You get the speed boost without rewriting your ML code.

3. Test on a subset. Run both versions on 10% of your data and compare results. Polars and Pandas handle edge cases differently (nulls, duplicates, sorting).

4. Watch for silent behavior changes. Check for:
   – Column order after joins (Polars doesn’t preserve it)
   – Null handling in aggregations (Polars skips nulls by default, same as Pandas, but check your .agg() calls)
   – String dtype inference (Polars uses Utf8, Pandas uses object)

I migrated a 5-stage ETL pipeline over two months. Stage 1 (large join) went to Polars first—40 minute runtime dropped to 4 minutes. Stage 3 (string parsing) stayed on Pandas because it was already fast. Stage 5 (model training) still uses Pandas because scikit-learn expects it.

Hybrid is fine. Use the right tool per stage.

The Compatibility Tax

Polars 1.0 shipped in July 2024. The API is stable now, but the ecosystem is still catching up. You’ll hit this:

import polars as pl
from sklearn.ensemble import RandomForestClassifier

# Polars DataFrame
df = pl.read_parquet("train.parquet")
X = df.select(["feature1", "feature2", "feature3"])
y = df["target"]

# scikit-learn expects numpy or pandas
model = RandomForestClassifier()
model.fit(X, y)  # TypeError: expected numpy array or pandas DataFrame

# You need to convert
model.fit(X.to_numpy(), y.to_numpy())  # Works, but adds overhead

This conversion is cheap for small DataFrames (<10K rows), but it defeats the purpose if you’re processing 10M rows in Polars just to convert back to NumPy.

Some libraries are adding Polars support (Great Expectations, Altair, plotly). But most of the ML stack (scikit-learn, XGBoost, LightGBM) still expects Pandas or NumPy.

If you’re doing ML, you’ll be converting back and forth. Factor that into your performance calculations.

What About Polars Plugins?

Polars has a plugin system for extending it with custom expressions. You write Rust code, compile it, and call it from Python. This is powerful if you have domain-specific operations that need to be fast.

I haven’t used it in production yet. The documentation is sparse, and the learning curve is steep (you need to know Rust). But I’ve seen teams use it for:

• Custom distance metrics (e.g., Haversine for geospatial data)
• Domain-specific parsers (e.g., parsing binary log formats)
• Optimized rolling window functions

If you’re already comfortable with Rust, this is a huge advantage over Pandas (which requires writing C extensions). If you’re not, it’s a non-factor.

Benchmarks That Actually Matter

Forget “Polars is 10x faster.” Ask:

• What’s your bottleneck? If it’s I/O, neither Pandas nor Polars will help—you need faster disk or compression.

• What’s your dataset size? Under 100MB? Performance is noise. Over 10GB? Polars wins decisively.

• What operations dominate? Joins and aggregations favor Polars. String ops and ML integration favor Pandas.

• What’s your deployment target? If you’re shipping to clients with strict dependency lists, Pandas is safer.

Run your own benchmarks. Use your actual data, your actual queries. Synthetic benchmarks lie.

FAQ

Q: Can I use Polars with Jupyter notebooks?

Yes. Polars DataFrames render nicely in Jupyter. You lose some of the interactive exploration Pandas offers (like tab-completion for column names in some IDEs), but it works fine. I use both interchangeably.

Q: Does Polars support GPU acceleration?

No, not natively. Polars is CPU-only but highly parallelized. If you need GPU, look at cuDF (NVIDIA’s GPU DataFrame library). But for most workloads, Polars’s multi-core CPU execution is faster than single-GPU cuDF unless you’re on an A100.

Q: What if I have existing Pandas code in production?

Migrate incrementally. Start with the slowest part of your pipeline. Keep the rest on Pandas. Use .to_pandas() and .from_pandas() at the boundaries. You don’t have to rewrite everything at once—I didn’t, and it worked fine.

What I’d Do Next Time

If I were starting a new data pipeline today, I’d default to Polars for ETL and Pandas for ML. The hybrid approach avoids migration debt and lets you use the best tool per stage.

But I’m curious about the next generation of tools. DuckDB is eating into Polars’s niche with SQL-based lazy evaluation. DataFusion (the Rust library Polars uses internally) is getting a Python API. The landscape is moving fast.

For now, Polars is the best general-purpose replacement for Pandas at scale. But “at scale” is the key qualifier. If your data fits in RAM and your runtime is under a minute, stick with Pandas. The ecosystem is worth more than the speed.

And if you’re switching, test your edge cases. I found three silent behavior changes in my migration that only showed up in production. Polars is fast, but it’s not a drop-in replacement.