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, string_cache: bool = False, no_optimization: bool = False, slice_pushdown: bool = True, common_subplan_elimination: bool = True) DataFrame[source]#

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.

string_cache

This argument is deprecated. Please set the string cache globally. The argument will be ignored

no_optimization

Turn off optimizations.

slice_pushdown

Slice pushdown optimization

common_subplan_elimination

Will try to cache branching subplans that occur on self-joins or unions.

Returns:
DataFrame