polars.LazyFrame.collect#

LazyFrame.collect( *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, no_optimization: bool = False, streaming: bool = False, _eager: bool = False, ) → DataFrame[source]#

Materialize this LazyFrame into a DataFrame.

By default, all query optimizations are enabled. Individual optimizations may be disabled by setting the corresponding parameter to False.

Parameters:

type_coercion: Do type coercion optimization.
predicate_pushdown: Do predicate pushdown optimization.
projection_pushdown: Do projection pushdown optimization.
simplify_expression: Run simplify expressions optimization.
slice_pushdown: Slice pushdown optimization.
comm_subplan_elim: Will try to cache branching subplans that occur on self-joins or unions.
comm_subexpr_elim: Common subexpressions will be cached and reused.
no_optimization: Turn off (certain) optimizations.
streaming: Process the query in batches to handle larger-than-memory data. If set to False (default), the entire query is processed in a single batch.

Warning

This functionality is currently in an alpha state.

Note

Use explain() to see if Polars can process the query in streaming mode.

Returns:

DataFrame

See also

fetch: Run the query on the first n rows only for debugging purposes.
explain: Print the query plan that is evaluated with collect.
profile: Collect the LazyFrame and time each node in the computation graph.
polars.collect_all: Collect multiple LazyFrames at the same time.
polars.Config.set_streaming_chunk_size: Set the size of streaming batches.

Examples

>>> lf = pl.LazyFrame(
...     {
...         "a": ["a", "b", "a", "b", "b", "c"],
...         "b": [1, 2, 3, 4, 5, 6],
...         "c": [6, 5, 4, 3, 2, 1],
...     }
... )
>>> lf.group_by("a").agg(pl.all().sum()).collect()  
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 4   ┆ 10  │
│ b   ┆ 11  ┆ 10  │
│ c   ┆ 6   ┆ 1   │
└─────┴─────┴─────┘

Collect in streaming mode

>>> lf.group_by("a").agg(pl.all().sum()).collect(
...     streaming=True
... )  
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 4   ┆ 10  │
│ b   ┆ 11  ┆ 10  │
│ c   ┆ 6   ┆ 1   │
└─────┴─────┴─────┘