sum#

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

Compute grouped sum aggregation.

Examples

>>> import ray
>>> ray.data.from_items([
...     (i % 3, i, i**2)
...     for i in range(100)])
...     .groupby(lambda x: x[0] % 3)
...     .sum(lambda x: x[2])
>>> ray.data.range(100).groupby("id").sum()
>>> ray.data.from_items([
...     {"A": i % 3, "B": i, "C": i**2}
...     for i in range(100)])
...     .groupby("A")
...     .sum(["B", "C"])
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 sum; 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 sum result.

For different values of on, the return varies:

  • on=None: a dataset containing a groupby key column, "k", and a column-wise sum 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.

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