polars.LazyFrame.show_graph#

LazyFrame.show_graph( *, optimized: bool = True, show: bool = True, output_path: str | Path | None = None, raw_output: bool = False, figsize: tuple[float, float] = (16.0, 12.0), 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, ) → str | None[source]#

Show a plot of the query plan.

Note that graphviz must be installed to render the visualization (if not already present you can download it here: <https://graphviz.org/download>`_).

Parameters:

optimized: Optimize the query plan.
show: Show the figure.
output_path: Write the figure to disk.
raw_output: Return dot syntax. This cannot be combined with show and/or output_path.
figsize: Passed to matplotlib if show == True.
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)

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"
... ).show_graph()  