GroupBy.nth(self, n: Union[int, List[int]], dropna: Union[str, NoneType] = None) → pandas.core.frame.DataFrame
[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: |
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Returns: |
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See also
Series.groupby
DataFrame.groupby
>>> 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
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https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.core.groupby.GroupBy.nth.html