pandas.core.groupby.SeriesGroupBy.nth#
- 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: []