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1. Start with a lazy scan and collect once
When working with file-backed data, use a scan such as scan_parquet or scan_csv to create a LazyFrame. Add filters, select only the columns you need, and perform aggregations before calling collect() when you actually need an in-memory result.
import polars as pl
result = (
pl.scan_parquet("events.parquet")
.filter(pl.col("event_date") >= pl.date(2025, 1, 1))
.select("event_date", "account_id", "amount")
.group_by("account_id")
.agg(pl.col("amount").sum())
.collect()
)This is an illustrative pattern, not a benchmark; use the columns and conditions your task requires. A lazy query gives Polars a chance to optimize the full chain before execution. For example, predicate pushdown can move a filter closer to the scan, while projection pushdown can avoid reading columns the query does not need. The Polars lazy API guide says that deferring execution can offer significant performance advantages and that the lazy API is preferred in most cases.
If your data is already in a regular in-memory DataFrame, calling .lazy() lets you build the remaining work lazily. It does not reverse the cost of loading the data into memory in the first place. Polars describes both approaches in its lazy usage guide.
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2. Use native expressions and inspect the query plan
Describe transformations with Polars expressions in contexts such as select and with_columns, rather than defaulting to Python row-by-row loops. Expressions can be simplified in context, and independent expressions may be evaluated in parallel. For repeated work across known types, expression expansion can target matching columns. See the expressions and contexts guide.
For a lazy query, call explain() to inspect the planned operations:
query = (
pl.scan_csv("events.csv")
.filter(pl.col("amount") > 0)
.select("account_id", "amount")
)
print(query.explain())Look for the filter and required-column selection near the scan when those rewrites are applicable. The exact plan depends on the query and source; explain() is a way to check what Polars planned, not proof that a particular operation will always be pushed into a reader. The optimizer guide also documents slice pushdown, common-subplan elimination, expression simplification, join ordering, type coercion, and cardinality estimation. These are optimizer behaviors, not switches most users need to set manually.
3. Choose streaming execution or a sink when memory is tight
If a query’s result is too large to materialize comfortably in memory, consider streaming execution or writing the result directly to storage. The execution guide documents collection with engine="streaming"; source-and-sink APIs can write results in batches instead of requiring a complete in-memory output.
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result = query.collect(engine="streaming")
# Or write a lazy result to storage in batches where supported
query.sink_parquet("filtered-events.parquet")Whether a query streams efficiently depends on the operators in its plan and the current Polars version. Check the relevant streaming concepts, sources and sinks guide, and query execution guide, then measure execution time and peak memory on your own workload. A sink is the natural choice when the output belongs in a file rather than in a Python variable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep version and result-order assumptions explicit
Do not assume a streaming default based on documentation for a different release. Polars’ surfaced 2.0 documentation is explicitly a release-candidate guide; it describes streaming as the lazy API default for that version and warns that streaming may not preserve row order for operations that do not require it, including group-by and joins. That is not a blanket statement about stable releases. Check the documentation for the version you run, and sort explicitly when order matters. The warning appears in the Polars 2.0 release-candidate upgrade guide.
Also, reusing a LazyFrame in separate downstream queries does not guarantee that shared work is cached; Polars may recompute it. Inspect the plans and, when repeated expensive work warrants it, choose an intentional materialization or caching strategy that the current API supports. The query execution guide describes this reuse caveat.
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