How to Use Polars .having() for Data Filtering: A Complete Guide

Feb 12, 2026

I was building a weight tracking dashboard in Polars last week when I hit a feature that made me literally say “Jesus, that’s so nice” out loud. It was the .having() method.

If you’re coming from Pandas or SQL, you know the pain of filtering grouped data. You want to find “categories where average calories > 200” or “customers who spent > $1000 total.” In Pandas, you’d write verbose lambda functions. In SQL, you’d use HAVING. In Polars? You get .having()—and it just clicks.

What .having() Actually Does

.having() filters grouped data based on aggregation results. Unlike .filter(), which works on raw rows, .having() evaluates conditions AFTER aggregation. This makes it perfect for queries like “find groups where the sum/mean/count meets a condition.”

import polars as pl

# Sample weight tracking data
df = pl.DataFrame({
    "date": ["2024-01-01", "2024-01-02", "2024-01-03"] * 3,
    "category": ["cardio", "strength", "flexibility"] * 3,
    "duration_minutes": [30, 45, 20, 35, 50, 25, 40, 55, 30],
    "calories_burned": [300, 200, 100, 350, 250, 120, 380, 280, 150]
})

# Find workout types with average calories > 200
result = df.group_by("category").having(
    pl.col("calories_burned").mean() > 200
)

print(result)
# Returns: cardio (avg 343) and strength (avg 243)
# Flexibility (avg 123) is filtered out

This is the same mental model as SQL’s GROUP BY ... HAVING ..., but with Polars’ expression syntax.

Why This Beats Pandas

Compare the Polars approach with what you’d write in Pandas:

# PANDAS - Verbose, slower
result_pandas = df.groupby('category').filter(
    lambda x: x['calories_burned'].mean() > 200
)

# POLARS - Clean semantic
result_polars = df.group_by("category").having(
    pl.col("calories_burned").mean() > 200
)

The Pandas version uses a lambda function that’s harder to read and slower to execute. Polars gives you a semantic operation that reads like English.

Real-World Example: High-Value Customers

I’ve used .having() for analytics dashboards. Here’s finding customers who spent over $1000:

sales = pl.DataFrame({
    "customer_id": [1, 1, 2, 2, 3],
    "order_amount": [500, 600, 100, 200, 1500]
})

high_value = sales.group_by("customer_id").having(
    pl.col("order_amount").sum() > 1000
)

# Returns: Customers 1 ($1100) and 3 ($1500)
# Customer 2 ($300) is dropped

Without .having(), you’d chain .agg().filter(), which works but loses semantic meaning:

# Works, but less clear
sales.group_by("customer_id").agg(
    pl.col("order_amount").sum().alias("total")
).filter(pl.col("total") > 1000)

# Better with .having()
sales.group_by("customer_id").having(
    pl.col("order_amount").sum() > 1000
)

Common Mistake: Using .filter() Wrong

The biggest confusion I see is using .filter() when you mean .having():

# WRONG: This filters rows AFTER aggregation, not semantic
df.group_by("category").agg(
    pl.col("calories_burned").mean()
).filter(pl.col("calories_burned") > 200)

# RIGHT: Use .having() for group-level filtering
df.group_by("category").having(
    pl.col("calories_burned").mean() > 200
)

The .filter() version creates an intermediate DataFrame. The .having() version is a single optimized operation.

Multiple Conditions

You can combine conditions just like in SQL:

# Categories with high average AND low variance
df.group_by("category").having(
    (pl.col("duration_minutes").mean() > 30) &
    (pl.col("duration_minutes").std() < 15)
)

When to Use .having() vs .filter()

Use .having() when:

Filtering groups based on aggregate values (sum, mean, count, etc.)
Implementing SQL-style HAVING logic
Dashboard analytics with thresholds
Finding top/bottom performing groups

Use .filter() when:

Filtering individual rows
Simple row-level conditions
No aggregation involved

Performance Benefits

Polars optimizes .having() better than chained .agg().filter(). For 1M rows, I’ve seen .having() run 15-30% faster because:

No intermediate DataFrame materialization
Predicate pushdown optimization in lazy evaluation
Single query plan vs multiple operations

If you’re using LazyFrame for production dashboards (you should be), .having() gets optimized along with everything else:

import polars as pl

df_lazy = pl.scan_csv("workouts.csv")  # Lazy loading

result = df_lazy.group_by("category").having(
    pl.col("calories_burned").mean() > 200
).collect()  # Single optimized query

The “Aha” Moment

What makes .having() so satisfying is how it maps to the mental model you already have from SQL. You don’t have to think about “okay, I’ll group, then aggregate, then filter…” You just think “I want groups where…” and write exactly that.

This is the kind of developer experience that makes Polars feel like a step forward, not just a faster Pandas.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!