pandas.core.groupby.SeriesGroupBy.nth — pandas 2.2.3 documentation (original) (raw)

property SeriesGroupBy.nth[source]#

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

Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices.

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.

Parameters:

nint, slice or list of ints and slices

A single nth value for the row or a list of nth values or slices.

Changed in version 1.4.0: Added slice and lists containing slices. Added index notation.

dropna{‘any’, ‘all’, None}, default None

Apply the specified dropna operation before counting which row is the nth row. Only supported if n is an int.

Returns:

Series or DataFrame

N-th value within each group.

See also

Series.groupby

Apply a function groupby to a Series.

DataFrame.groupby

Apply a function groupby 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

Index notation may also be used

g.nth[0, 1] A B 0 1 NaN 1 1 2.0 2 2 3.0 4 2 5.0 g.nth[:-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: []