Polars is a high-performance DataFrame library that uses lazy evaluation and parallel execution to outperform pandas on large datasets. Understanding expressions, contexts, and the lazy API is key for data engineers.
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A data engineer writes df.lazy().filter(pl.col('age') > 30).select(['name', 'age']).collect(). What is the key benefit of using .lazy() before .collect()?
Polars' lazy API constructs a logical query plan that the query optimizer can rewrite before execution. Optimizations include predicate pushdown (filter early to reduce rows) and projection pruning (read only needed columns). .collect() triggers the actual computation on the optimized plan.
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What is the difference between Polars expressions and pandas-style column operations?
Polars expressions (built with pl.col(), pl.lit(), etc.) are lazy, composable, and automatically parallelized. Unlike pandas operations which execute immediately row-by-row, Polars expressions are evaluated by a multithreaded engine that processes entire column chunks in parallel, achieving much higher throughput.
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A Polars user calls df.group_by('category').agg(pl.col('value').mean()). Which Polars execution context does this use?
Polars has distinct expression contexts: select/with_columns operate on whole columns, filter returns boolean masks, and the group_by aggregation context allows aggregation expressions like .mean(), .sum(), or .list(). Using an aggregation expression outside its context raises an error.
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How does pl.scan_parquet('data/*.parquet') differ from pl.read_parquet('data/*.parquet')?
pl.scan_parquet() returns a LazyFrame — no data is read from disk yet. Only when .collect() is called does Polars execute the optimized plan, potentially reading only the needed columns and row groups (predicate pushdown into Parquet metadata). pl.read_parquet() immediately loads all data into a DataFrame.
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A developer needs to add a new column that is the sum of two existing columns. Which Polars approach is correct?
df.with_columns() is the idiomatic Polars way to add or transform columns. It accepts expressions and returns a new DataFrame (Polars DataFrames are immutable). The .alias('total') names the new column. Unlike pandas' df['col'] = ..., this avoids mutation and integrates with the lazy query planner.