pandas.core.groupby.GroupBy.nth#

final GroupBy.nth(n, dropna=None)[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)
     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
>>> g.nth(slice(None, -1))
     B
A
1  NaN
1  2.0
2  3.0

Index notation may also be used

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

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