pandas.core.groupby.GroupBy.nth¶
- GroupBy.nth(n, dropna=None)[source]¶
Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints.
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 or list of ints
A single nth value for the row or a list of nth values.
- dropna{‘any’, ‘all’, None}, default None
Apply the specified dropna operation before counting which row is the nth row.
- 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) B A 1 NaN 2 3.0 >>> g.nth(1) B A 1 2.0 2 5.0 >>> g.nth(-1) B A 1 4.0 2 5.0 >>> g.nth([0, 1]) B A 1 NaN 1 2.0 2 3.0 2 5.0
Specifying dropna allows count ignoring
NaN
>>> g.nth(0, dropna='any') B A 1 2.0 2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any') B A 1 NaN 2 NaN
Specifying as_index=False in groupby keeps the original index.
>>> df.groupby('A', as_index=False).nth(1) A B 1 1 2.0 4 2 5.0