BUG: in _nsorted for frame with duplicated values index

[Tux1]

The function below has been incorrectly implemented. If the frame has an index with duplicated values, you will get a result with more than n rows and not properly sorted. So nsmallest and nlargest for DataFrame doesn't return a correct frame in this particular case.

def _nsorted(self, columns, n, method, keep):
    if not com.is_list_like(columns):
        columns = [columns]
    columns = list(columns)
    ser = getattr(self[columns[0]], method)(n, keep=keep)
    ascending = dict(nlargest=False, nsmallest=True)[method]
    return self.loc[ser.index].sort_values(columns, ascending=ascending,
                                           kind='mergesort')

[jorisvandenbossche]

Indeed:

In [71]: df = pd.DataFrame({'a':[1,2,3,4], 'b':[4,3,2,1]}, index=[0,0,1,1])

In [72]: df.nlargest(1, 'a')
Out[72]:
   a  b
1  4  1
1  3  2

In [73]: df.nlargest(2, 'a')
Out[73]:
   a  b
1  4  1
1  4  1
1  3  2
1  3  2

(@Tux1 side note for future reference, it is always nice to provide a small reproducible example when opening an issue)
Interested in doing a PR to fix this?

[Tux1]

Yes I will fix that soon
Sorry about example

Le 9 juin 2016 à 23:30, Joris Van den Bossche notifications@github.com a écrit :

Indeed:

In [71]: df = pd.DataFrame({'a':[1,2,3,4], 'b':[4,3,2,1]}, index=[0,0,1,1])

In [72]: df.nlargest(1, 'a')
Out[72]:
a b
1 4 1
1 3 2

In [73]: df.nlargest(2, 'a')
Out[73]:
a b
1 4 1
1 4 1
1 3 2
1 3 2
(@Tux1 side note for future reference, it is always nice to provide a small reproducible example when opening an issue)
Interested in doing a PR to fix this?

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[Tux1]

my fix is not very elegant but I don't see any other solution to deal with MultiIndex and duplicated value index

[shankararul]

Sum seems to work fine in .19.2 But with count, it doesn't seem to make sense. The df gets repeated as many times as the "n". Is that a bug or am i doing something wrong ?

df.groupby(['a']).agg({'b':'count'}).nlargest(2, 'b')

	b
a	
1	1
2	1
3	1
4	1
1	1
2	1
3	1
4	1

[jreback]

@shankararul see: #15297