Polars — When Pandas Hits a Wall · Data Engineering Quest

The Rust-backed alternative

Polars (1.39.3 as of April 2026) is a DataFrame library written in Rust with Python bindings. It uses Arrow as its in-memory format, executes queries in parallel by default, and has both an eager API (familiar to Pandas users) and a lazy API (queries are built up as a plan, optimized, and executed in one pass — like SQL).

When to reach for it over Pandas

Your data is bigger than memory, but you don't need a distributed system. pl.scan_* with lazy eval streams.
You're doing many transformations and Pandas is taking minutes. Polars's parallel execution often closes that to seconds.
You want a query planner. Polars optimizes; Pandas executes literally what you wrote.
You're running on a many-core machine and Pandas is leaving cores idle.

When to stick with Pandas

Tons of ecosystem libraries (statsmodels, sklearn, plotnine) accept Pandas DataFrames natively.
The data fits comfortably in memory and is small enough that the speedup doesn't matter.
You're doing exploratory work and need to print things mid-chain — Polars's lazy plans don't give you intermediate results without .collect().

Code

Polars eager API — feels like Pandas, but parallel by default·python

import polars as pl

df = pl.read_parquet('orders.parquet')

monthly = (
    df.filter(pl.col('status') == 'completed')
      .with_columns(pl.col('order_date').dt.truncate('1mo').alias('month'))
      .group_by('month')
      .agg([
          pl.col('amount_usd').sum().alias('revenue'),
          pl.col('order_id').n_unique().alias('orders'),
      ])
      .sort('month')
)

Polars lazy API — build a query, optimize, execute in one pass·python

import polars as pl

monthly = (
    pl.scan_parquet('warehouse/orders/year=2026/**/*.parquet')
      .filter(pl.col('status') == 'completed')
      .with_columns(pl.col('order_date').dt.truncate('1mo').alias('month'))
      .group_by('month')
      .agg([
          pl.col('amount_usd').sum().alias('revenue'),
          pl.col('order_id').n_unique().alias('orders'),
      ])
      .sort('month')
      .collect(streaming=True)   # streams in chunks; bigger-than-memory works
)

# Inspect the optimized plan that Polars actually ran
print(
    pl.scan_parquet('warehouse/orders/year=2026/**/*.parquet')
      .filter(pl.col('status') == 'completed')
      .explain(optimized=True)
)

Polars — When Pandas Hits a Wall

The Rust-backed alternative

When to reach for it over Pandas

When to stick with Pandas

Code

External links

Exercise

Progress

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