Polars Lazy API: collect(), fetch(), and describe_plan()

So far, we’ve used Polars in “Eager” mode (like Pandas), where df.filter() runs immediately. However, the Polars Lazy API offers a different approach to working with data by deferring execution until needed.

The real power of Polars is “Lazy” mode. In Lazy mode, you build a query plan first, and Polars finds the fastest way to run it. This allows Polars to handle datasets larger than your RAM.

Eager vs. Lazy

• Eager: pl.read_csv() -> Loads 10GB into RAM. THEN df.filter() -> Creates a new 5GB DataFrame in RAM. (Uses 15GB RAM)
• Lazy: pl.scan_csv() -> Loads nothing. THEN .filter() -> Loads nothing. THEN .collect() -> Runs an optimized plan that only loads the 5GB you actually needed. (Uses 5GB RAM)

Step 1: scan_ (The Lazy Start)

You start a Lazy query by “scanning” a file instead of “reading” it.

import polars as pl

# This loads NOTHING into memory. It just "scans" the file.
lazy_df = pl.scan_csv("my_large_data.csv")

lazy_df is now a LazyFrame object (a query plan).

Step 2: Build the Plan

Now, we chain all our expressions. No code is running yet!

query_plan = (
    lazy_df
    .filter(pl.col("age") > 30)
    .group_by("department")
    .agg(pl.col("salary").mean())
)

Step 3: See the Plan

You can even ask Polars what its optimized plan is:

print(query_plan.describe_plan())
# It will show you an optimized query tree!

Step 4: Run the Plan (collect or fetch)

When you are ready for the answer, you “collect” the results.

• .collect(): Runs the full query and brings all results into memory.
• .fetch(n): Runs the query but only brings back the first n rows.

# NOW Polars will actually read the file and run the query
results = query_plan.collect()
print(results)

This is the key to high-performance data science in 2026.