Polars Tutorial: A Transaction-Flow Pipeline

Setup and scan

import polars as pl

txns = pl.scan_parquet("transactions.parquet")
print(txns.describe_optimized_plan())

Lazy execution defers work until collect(), letting Polars push down filters and prune columns. The plan shown by describe_optimized_plan() is the contract: whatever the optimizer proves it can eliminate — columns never read, predicates applied before joins — never touches the data. Starting every pipeline with scan_parquet instead of read_parquet makes this optimization available by default.

Transform with the lazy API

daily = (
 txns
 .filter(pl.col("amount") > 0)
 .group_by("date", "account_id")
 .agg(pl.col("amount").sum().alias("total"))
 .sort("total", descending=True)
)
result = daily.collect()

The filter runs before the group-by, so the aggregator never sees discarded rows. Column pruning also means the parquet reader loads only amount, date, and account_id — on wide transaction files this alone is often a 10x I/O reduction.

Joins and window functions

accts = pl.read_csv("accounts.csv")
joined = result.join(accts, on="account_id", how="left")
with_rank = joined.with_columns(
 pl.col("total").rank(method="dense").over("country").alias("country_rank")
)

Polars over-conditions are equivalent to SQL window functions — ideal for rolling aggregates like 30-day velocity. Profile with explain() before tuning: most bottlenecks are join order and filter placement, not the engine itself. A left join after aggregation keeps the data volume small; joining before would multiply rows by account history length.

Collect and iterate

collect() materialises the plan, but collect(streaming=True) processes it in bounded-memory batches when datasets exceed RAM. For interactive exploration, .limit(1000) before collect gives a fast sample without changing the plan's correctness. A transaction-flow pipeline that runs once on full data and repeatedly on fresh increments benefits from keeping the plan lazy until the last moment.

References

• Polars User Guide — Lazy API(official docs)
• Polars API Reference(official docs)
• pola-rs/polars GitHub Repository(official docs)