polars.LazyFrame.fetch#
- LazyFrame.fetch(
- n_rows: int = 500,
- *,
- type_coercion: bool = True,
- predicate_pushdown: bool = True,
- projection_pushdown: bool = True,
- simplify_expression: bool = True,
- no_optimization: bool = False,
- slice_pushdown: bool = True,
- comm_subplan_elim: bool = True,
- comm_subexpr_elim: bool = True,
- streaming: bool = False,
Collect a small number of rows for debugging purposes.
Fetch is like a
collect()operation, but it overwrites the number of rows read by every scan operation. This is a utility that helps debug a query on a smaller number of rows.Note that the fetch does not guarantee the final number of rows in the DataFrame. Filter, join operations and a lower number of rows available in the scanned file influence the final number of rows.
- Parameters:
- n_rows
Collect n_rows from the data sources.
- type_coercion
Run type coercion optimization.
- predicate_pushdown
Run predicate pushdown optimization.
- projection_pushdown
Run projection pushdown optimization.
- simplify_expression
Run simplify expressions optimization.
- no_optimization
Turn off optimizations.
- 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.
- streaming
Run parts of the query in a streaming fashion (this is in an alpha state)
- Returns:
- DataFrame
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.groupby("a", maintain_order=True).agg(pl.all().sum()).fetch(2) shape: (2, 3) ┌─────┬─────┬─────┐ │ a ┆ b ┆ c │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 │ ╞═════╪═════╪═════╡ │ a ┆ 1 ┆ 6 │ │ b ┆ 2 ┆ 5 │ └─────┴─────┴─────┘