Get comfortable with Polars' lazy evaluation and query optimization.
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At standup, a dev wants Polars to optimize a whole query before executing. Which API enables this?
A LazyFrame builds a query plan without executing immediately, unlike the eager DataFrame. Polars can then apply optimizations like predicate and projection pushdown. You trigger execution explicitly later.
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During a design review, the team reads a huge CSV lazily to avoid loading it all up front. Which function fits?
scan_csv returns a LazyFrame and defers reading, enabling pushdown so only needed rows and columns are loaded. In contrast, read_csv loads eagerly into memory. Scanning is preferred for large files.
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In a PR review, a dev built a lazy plan but sees no result. What must they call to run it?
A LazyFrame stays a plan until you call collect(), which executes the optimized query and materializes a DataFrame. Without collect(), no computation happens. This explicit boundary is the heart of the lazy evaluation model.
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During a code review, a dataset is larger than RAM. Which collect option processes it in chunks?
Calling collect(streaming=True) runs the query in a streaming engine that processes data in batches rather than all at once. This lets Polars handle datasets larger than memory. It is a key reason to prefer the lazy API for big data.
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An incident report shows an eager pipeline ran out of memory on a join. What lazy advantage was missed?
The lazy evaluation path lets Polars optimize the plan, pushing filters and column selection down to reduce data scanned before a join. Eager execution applies steps immediately with no global optimization. Switching to LazyFrame often avoids the memory blowup.