pandas.api.typing.DataFrameGroupBy.last#

DataFrameGroupBy.last(numeric_only=False, min_count=-1, skipna=True)[source]#

Compute the last entry of each column within each group.

NA values are skipped by default, so the result is the last non-NA value per column — which may differ from the last row of the group. Pass skipna=False to keep NAs.

Parameters:

numeric_onlybool, default False: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_countint, default -1: The required number of valid values to perform the operation. If fewer than min_count valid values are present the result will be NA.
skipnabool, default True: Exclude NA values. If an entire group is NA, the result will be NA.

Added in version 2.2.1.

Returns:

Series or DataFrame: Last values within each group.

See also

DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.
api.typing.DataFrameGroupBy.first: Compute the first entry of each column within each group.
api.typing.DataFrameGroupBy.nth: Take the nth row from each group.

Examples

>>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3]))
>>> df.groupby("A").last()
     B  C
A
1  5.0  2
3  6.0  3