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,
collapse_joins: 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.

collapse_joins

Collapse a join and filters into a faster join.

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()