GroupBy Operations — Pandas vs Polars in Real Data Workflows

If you work with data, you group things — a lot.

Whether it’s aggregating metrics, summarizing logs, or preparing features for analysis, groupby operations sit at the heart of most data workflows.

Both Pandas and Polars support powerful groupby operations.
But the way they approach grouping — and how that impacts performance and clarity — is quite different.

GroupBy in Pandas: familiar and flexible

Pandas’ groupby API is one of its strongest features.

import pandas as pd

df = pd.DataFrame({
    "category": ["A", "A", "B", "B"],
    "value": [10, 20, 30, 40]
})

result = df.groupby("category")["value"].sum()
print(result)

This style is:

• expressive
• flexible
• deeply integrated with the Pandas ecosystem

For exploratory analysis and feature engineering, it feels natural.

However, Pandas groupby:

• executes eagerly
• creates intermediate objects
• can become slow on large datasets

GroupBy in Polars: explicit and optimized

Polars takes a more declarative approach.

import polars as pl

df = pl.DataFrame({
    "category": ["A", "A", "B", "B"],
    "value": [10, 20, 30, 40]
})

result = (
    df.group_by("category")
      .agg(pl.col("value").sum())
)

print(result)

Here, aggregation is:

• explicit
• column-oriented
• designed for optimization

This approach works especially well in larger pipelines.

Lazy groupby in Polars

One major difference appears when using lazy execution:

lazy_df = (
    df.lazy()
      .group_by("category")
      .agg(pl.col("value").sum())
)

Nothing runs until .collect() is called.

This allows Polars to:

• reorder operations
• combine transformations
• reduce memory usage

In long pipelines, this can make a substantial difference.

Readability vs predictability

• Pandas prioritizes flexibility and interactive usage
• Polars prioritizes predictability and performance

Neither approach is inherently better — they target different needs.

Real-world takeaway

In practice:

• Use Pandas groupby for exploration and ML workflows
• Use Polars groupby for heavy aggregations and ETL pipelines
• Lazy execution amplifies Polars’ advantage at scale

Conclusion

GroupBy operations highlight the philosophical difference between Pandas and Polars.

Pandas feels dynamic and flexible.
Polars feels intentional and optimized.

Choosing between them depends less on syntax — and more on the shape of your data pipeline.