polars.LazyFrame.explain#

LazyFrame.explain( *, format: ExplainFormat = 'plain', optimized: bool = True, 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, cluster_with_columns: bool = True, streaming: bool = False, tree_format: bool | None = None, ) → str[source]#

Create a string representation of the query plan.

Different optimizations can be turned on or off.

Parameters:

format{‘plain’, ‘tree’}: The format to use for displaying the logical plan.
optimized: Return an optimized query plan. Defaults to True. If this is set to True the subsequent optimization flags control which optimizations run.
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.
cluster_with_columns: Combine sequential independent calls to with_columns
streaming: Run parts of the query in a streaming fashion (this is in an alpha state)
tree_format: Format the output as a tree.

Deprecated since version 0.20.30: Use format="tree" instead.

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", maintain_order=True).agg(pl.all().sum()).sort(
...     "a"
... ).explain()  