ray.data.grouped_data.GroupedData.std#

GroupedData.std(on: str | List[str] = None, ddof: int = 1, ignore_nulls: bool = True) → Dataset[source]#

Compute grouped standard deviation aggregation.

Examples

>>> import ray
>>> ray.data.range(100).groupby("id").std(ddof=0) 
>>> ray.data.from_items([ 
...     {"A": i % 3, "B": i, "C": i**2} 
...     for i in range(100)]) \ 
...     .groupby("A") \ 
...     .std(["B", "C"])

NOTE: This uses Welford’s online method for an accumulator-style computation of the standard deviation. This method was chosen due to it’s numerical stability, and it being computable in a single pass. This may give different (but more accurate) results than NumPy, Pandas, and sklearn, which use a less numerically stable two-pass algorithm. See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford’s_online_algorithm

Parameters:

  • on – a column name or a list of column names to aggregate.

  • ddof – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • ignore_nulls – Whether to ignore null values. If True, null values will be ignored when computing the std; if False, if a null value is encountered, the output will be null. We consider np.nan, None, and pd.NaT to be null values. Default is True.

Returns:

The standard deviation result.

For different values of on, the return varies:

  • on=None: a dataset containing a groupby key column, "k", and a column-wise std column for each original column in the dataset.

  • on=["col_1", ..., "col_n"]: a dataset of n + 1 columns where the first column is the groupby key and the second through n + 1 columns are the results of the aggregations.

If groupby key is None then the key part of return is omitted.