xorbits.pandas.groupby.SeriesGroupBy.nth#

SeriesGroupBy.nth(n, dropna=None)#

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

  • n (int, 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(pandas): 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

N-th value within each group.

Return type

Series or DataFrame

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: []

This docstring was copied from pandas.core.groupby.generic.SeriesGroupBy.