ray.data.Dataset.min
ray.data.Dataset.min#
- Dataset.min(on: Optional[Union[str, List[str]]] = None, ignore_nulls: bool = True) Union[Any, Dict[str, Any]] [source]#
Compute minimum over entire dataset.
Note
This operation will trigger execution of the lazy transformations performed on this dataset.
Examples
>>> import ray >>> ray.data.range(100).min("id") 0 >>> ray.data.from_items([ ... {"A": i, "B": i**2} ... for i in range(100)]).min(["A", "B"]) {'min(A)': 0, 'min(B)': 0}
- Parameters
on – a column name or a list of column names to aggregate.
ignore_nulls – Whether to ignore null values. If
True
, null values will be ignored when computing the min; ifFalse
, if a null value is encountered, the output will be None. We consider np.nan, None, and pd.NaT to be null values. Default isTrue
.
- Returns
The min result.
For different values of
on
, the return varies:on=None
: an dict containing the column-wise min of all columns,on="col"
: a scalar representing the min of all items in column"col"
,on=["col_1", ..., "col_n"]
: an n-column dict containing the column-wise min of the provided columns.
If the dataset is empty, all values are null, or any value is null AND
ignore_nulls
isFalse
, then the output will be None.