pandas.api.typing.DataFrameGroupBy.nth#

property DataFrameGroupBy.nth[source]#

Take the nth row from each group if n is an int, otherwise a subset of rows.

Deprecated since version 3.1.0: Index notation (g.nth[n]) is deprecated in favor of calling g.nth(n) and will be removed in a future version of pandas.

If dropna, will take the nth non-null row, dropna is either ‘all’ or ‘any’; this is equivalent to calling dropna(how=dropna) before the groupby.

Returns:

Series or DataFrame: N-th value within each group.

See also

Series.nth: Apply function nth to a Series.
DataFrame.nth: Apply function nth to each row or column of a DataFrame.

Examples

>>> df = pd.DataFrame(
...     {"A": [1, 1, 2, 1, 2], "B": [np.nan, 2, 3, 4, 5]}, columns=["A", "B"]
... )
>>> g = df.groupby("A")
>>> g.nth(0)
   A   B
0  1 NaN
2  2 3.0
>>> g.nth(1)
   A   B
1  1 2.0
4  2 5.0
>>> g.nth(-1)
   A   B
3  1 4.0
4  2 5.0
>>> g.nth([0, 1])
   A   B
0  1 NaN
1  1 2.0
2  2 3.0
4  2 5.0
>>> g.nth(slice(None, -1))
   A   B
0  1 NaN
1  1 2.0
2  2 3.0

Specifying dropna allows ignoring NaN values

>>> g.nth(0, dropna="any")
   A   B
1  1 2.0
2  2 3.0

When the specified n is larger than any of the groups, an empty DataFrame is returned

>>> g.nth(3, dropna="any")
Empty DataFrame
Columns: [A, B]
Index: []