SeriesGroupBy.agg

SeriesGroupBy.agg(func=None, engine=None, engine_kwargs=None, **kwargs) -> BodoDataFrame | BodoSeries

Apply one or more aggregate functions to groups of data in a single column from a BodoDataFrame. This method is the same as SeriesGroupBy.aggregate.

Parameters

func : function, str, list, dict or None: Function(s) to use for aggregating the data. Acceptable combinations are:

• A supported function e.g. sum
• The name of a supported aggregation function e.g. "sum"
• A list of functions, which will be applied to each selected column e.g. ["sum", "count"]
• None along with key word arguments specifying the supported functions to apply.

While providing a dictionary argument for func is supported, this use has been deprecated in Pandas and will raise an error in newer versions. Refer to our documentation for aggregate functions that are currently supported. Any other combination of arguments or user defined functions will either fallback to Pandas SeriesGroupBy.agg or raise a descriptive error.

**kwargs Key word arguments are used to create Named Aggregations and should be in the form new_name="function".

Note

The engine and engine_kwargs parameters are not supported, and will trigger a fallback to Pandas if specified.

Returns

BodoDataFrame or BodoSeries, depending on the value of func.

Examples

import bodo.pandas as bd

bdf1 = bd.DataFrame({
    "A": ["foo", "foo", "bar", "bar"],
    "C": [1, 2, 3, 4],
    "D": ["A", "A", "C", "D"]
})

bdf2 = bdf1.groupby("A")["C"].agg("sum")

print(bdf2)

Output:

A
bar    7
foo    3
Name: C, dtype: int64[pyarrow]

---

bdf3 = bdf1.groupby("A")["C"].agg(["sum", "count"])

print(bdf3)

Output:

     sum  count
A
bar    7      2
foo    3      2

---

bdf4 = bdf1.groupby("A")["C"].agg(sum_C="sum", mean_C="mean")

print(bdf4)

Output:

     sum_C  mean_C
A
bar      7     3.5
foo      3     1.5