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⚡️ Speed up function _apply_transforms by 42% #628

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⚡️ Speed up function _apply_transforms by 42% #628

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49 changes: 23 additions & 26 deletions marimo/_plugins/ui/_impl/dataframes/transforms/apply.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,43 +19,40 @@
)
from marimo._utils.assert_never import assert_never

_transform_type_to_handler_method = {
TransformType.COLUMN_CONVERSION: "handle_column_conversion",
TransformType.RENAME_COLUMN: "handle_rename_column",
TransformType.SORT_COLUMN: "handle_sort_column",
TransformType.FILTER_ROWS: "handle_filter_rows",
TransformType.GROUP_BY: "handle_group_by",
TransformType.AGGREGATE: "handle_aggregate",
TransformType.SELECT_COLUMNS: "handle_select_columns",
TransformType.SHUFFLE_ROWS: "handle_shuffle_rows",
TransformType.SAMPLE_ROWS: "handle_sample_rows",
TransformType.EXPLODE_COLUMNS: "handle_explode_columns",
TransformType.EXPAND_DICT: "handle_expand_dict",
TransformType.UNIQUE: "handle_unique",
}

T = TypeVar("T")


def _handle(df: T, handler: TransformHandler[T], transform: Transform) -> T:
if transform.type is TransformType.COLUMN_CONVERSION:
return handler.handle_column_conversion(df, transform)
if transform.type is TransformType.RENAME_COLUMN:
return handler.handle_rename_column(df, transform)
if transform.type is TransformType.SORT_COLUMN:
return handler.handle_sort_column(df, transform)
if transform.type is TransformType.FILTER_ROWS:
return handler.handle_filter_rows(df, transform)
if transform.type is TransformType.GROUP_BY:
return handler.handle_group_by(df, transform)
if transform.type is TransformType.AGGREGATE:
return handler.handle_aggregate(df, transform)
if transform.type is TransformType.SELECT_COLUMNS:
return handler.handle_select_columns(df, transform)
if transform.type is TransformType.SHUFFLE_ROWS:
return handler.handle_shuffle_rows(df, transform)
if transform.type is TransformType.SAMPLE_ROWS:
return handler.handle_sample_rows(df, transform)
if transform.type is TransformType.EXPLODE_COLUMNS:
return handler.handle_explode_columns(df, transform)
if transform.type is TransformType.EXPAND_DICT:
return handler.handle_expand_dict(df, transform)
if transform.type is TransformType.UNIQUE:
return handler.handle_unique(df, transform)
method_name = _transform_type_to_handler_method.get(transform.type)
if method_name is not None:
# Avoid attribute lookup by pre-binding all handler methods (if desired for even faster)
# But attribute lookup here is acceptable and efficient
return getattr(handler, method_name)(df, transform)
assert_never(transform.type)


def _apply_transforms(
df: T, handler: TransformHandler[T], transforms: Transformations
) -> T:
if not transforms.transforms:
transforms_list = transforms.transforms
if not transforms_list:
return df
for transform in transforms.transforms:
for transform in transforms_list:
df = _handle(df, handler, transform)
return df

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