Polars vs Pandas: Why Data Engineers Are Making the Switch in 2026

Pandas has been the workhorse of Python data processing for over a decade. It’s familiar, well-documented, and practically universal in data science workflows. But in 2026, a growing number of data engineers are quietly swapping it out for something faster, leaner, and more production-ready: Polars.

In this post, I’ll walk you through why the switch makes sense for data engineering pipelines, show you real code comparisons, and help you decide if Polars belongs in your stack.

What Is Polars and Why Is Everyone Talking About It?

Polars is a DataFrame library written in Rust with a Python API. Unlike Pandas — which is single-threaded and holds data in NumPy arrays — Polars is built on Apache Arrow memory format and uses a multi-threaded query engine with lazy evaluation.

• It uses all your CPU cores automatically
• It reads only the data it needs (lazy mode)
• Memory is managed far more efficiently

Benchmark: 10 Million Rows

Pandas

import pandas as pd
import time

start = time.time()
df = pd.read_parquet("transactions.parquet")
result = df.groupby("category")["amount"].sum()
print(f"Pandas: {time.time() - start:.2f}s")
# Output: Pandas: 4.31s

Polars (Lazy Mode)

import polars as pl
import time

start = time.time()
result = (
    pl.scan_parquet("transactions.parquet")
    .group_by("category")
    .agg(pl.col("amount").sum())
    .collect()
)
print(f"Polars: {time.time() - start:.2f}s")
# Output: Polars: 0.52s

8x faster. Not cherry-picked — this is consistent across typical aggregation and filter workloads.

Key Polars Features That Matter for Data Engineering

1. Lazy Evaluation with scan_*

df = (
    pl.scan_parquet("events.parquet")
    .filter(pl.col("event_date") > "2025-01-01")
    .select(["user_id", "event_date", "revenue"])
    .collect()
)

2. Parallel Expression Execution

df = df.with_columns([
    pl.col("price").mean().alias("avg_price"),
    pl.col("quantity").sum().alias("total_qty"),
    (pl.col("price") * pl.col("quantity")).alias("revenue")
])

3. Schema Enforcement

schema = {"user_id": pl.Int64, "amount": pl.Float64, "event_date": pl.Date}
df = pl.read_csv("data.csv", schema=schema)

4. No More SettingWithCopyWarning

Polars’ immutable design means transformations always return new DataFrames — no ambiguity, no surprises.

When to Still Use Pandas

• Quick EDA in Jupyter notebooks — Pandas’ df.describe() and plotting integrations are still more ergonomic
• Small datasets (<100K rows) — the startup overhead is not worth it
• Mature ML ecosystem — scikit-learn and XGBoost expect Pandas DataFrames

pandas_df = polars_df.to_pandas()

My Recommendation

If you’re building or maintaining production data pipelines that process more than a few million rows, give Polars a serious try. The performance gains are real, the API is clean, and the lazy evaluation model maps perfectly to how data engineering pipelines should work.

Have you tried Polars yet? Drop a comment below — I read every one.

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— Pushpjeet Cholkar, Data Engineer

Follow along on LinkedIn and Instagram @me_the_data_engineer for daily tips on Python, Data Engineering, and building a career in data.