"cannot reindex from a duplicate axis" when groupby().apply() on MultiIndex columns

[bdforbes]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

df = pd.DataFrame(
    np.ones([6, 4], dtype=int),
    columns=pd.MultiIndex.from_product([['A', 'B'], [1, 2]])
)

(
    df
    .groupby(level=0, axis=1)
    .apply(
        lambda df: pd.concat(
            [df.xs(df.name, axis=1), df.sum(axis=1).to_frame('Total')], 
            axis=1
        )
    )
)

Problem description

The code above produces the error:

cannot reindex from a duplicate axis

I believe this is a bug because, as described below, essentially the same operations can be successfully run along the other axis.

Expected Output

It should, as far as I can tell, produce the following output:

The desired output can be obtained when working on the transposed DataFrame along the index rather than the columns:

(
    df
    .T
    .groupby(level=0)
    .apply(
        lambda df: pd.concat(
            [df.xs(df.name), df.sum().to_frame('Total').T]
        )
    )
    .T
)

Output of pd.show_versions()

pd.show_versions()
pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.2
pytest: None
pip: 9.0.1
setuptools: 34.3.3
Cython: None
numpy: 1.13.0
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.9.5
s3fs: None
pandas_gbq: None
pandas_datareader: None

[bdforbes]

I've just found a simpler MWE. This fails:

(
    df
    .groupby(level=0, axis=1)
    .apply(
        lambda df: 2*df.xs(df.name, axis=1)
    )
)

This succeeds:

(
    df.T
    .groupby(level=0)
    .apply(
        lambda df: 2*df.xs(df.name)
    )
)

[jreback]

this is very very odd to odd. what are you actually trying to do?

[jreback]

for example your 'failing' ones:

Out[11]: 
A    [[1, 1, 2], [1, 1, 2], [1, 1, 2], [1, 1, 2], [...
B    [[1, 1, 2], [1, 1, 2], [1, 1, 2], [1, 1, 2], [...
dtype: object

In [10]: (
    ...:     df
    ...:     .groupby(level=0, axis=1)
    ...:     .apply(
    ...:         lambda df: 2*df.xs(df.name, axis=1).values
    ...:     )
    ...: )
Out[10]: 
A    [[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]]
B    [[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]]
dtype: object

Makes no sense to do this. You can certainly iterate over the groups yourself and concat (though you will find exactly the same issue if you don't drop the indexes). By definition pandas wants to align return objects.

[bdforbes]

I'm actually trying to adapt the code at https://stackoverflow.com/a/38964596/336001, which recursively adds subtotal rows into a DataFrame, to work along columns instead. Here is the original code, which adds subtotal rows:

def append_tot(df):
    if hasattr(df, 'name') and df.name is not None:
        xs = df.xs(df.name)
    else:
        xs = df
    gb = xs.groupby(level=0)
    n = xs.index.nlevels
    name = tuple('Total' if i == 0 else '' for i in range(n))
    tot = gb.sum().sum().rename(name).to_frame().T
    if n > 1:
        sm = gb.apply(append_tot)
    else:
        sm = gb.sum()
    return pd.concat([sm, tot])

The adapted version to add subtotal columns looks like this:

def append_tot(df):
    if hasattr(df, 'name') and df.name is not None:
        xs = df.xs(df.name, axis=1)
    else:
        xs = df
    gb = xs.groupby(level=0, axis=1)
    n = xs.columns.nlevels
    name = tuple('Total' if i == 0 else '' for i in range(n))
    tot = gb.sum().sum(axis=1).rename(name).to_frame()
    if n > 1:
        sm = gb.apply(append_tot)
    else:
        sm = gb.sum()
    return pd.concat([sm, tot], axis=1)

This code will produce the "cannot reindex from a duplicate index" error, whereas the original code provided in the Stack Overflow thread works fine.

The minimal working example I've found was just the simplest way to reproduce the error.

[kernc]

I'm facing the same issue when trying to interpolate each series within a group:

>>> df = pd.DataFrame({
...     'node': [59, 59, 59, 314, 314, 314, 59],
...     'ping': [116, np.nan, 106, 87, 80, np.nan, 118],
...     'mode': ['2G', np.nan, '4G', '3G', np.nan, '3G', '2G']},
...     columns=['node', 'ping', 'mode'],
...     index=pd.to_datetime(['2017-07-13 00:30:00',
...                           '2017-07-13 00:30:00',
...                           '2017-07-13 00:30:00',
...                           '2017-07-13 00:30:00',
...                           '2017-07-13 01:00:00',
...                           '2017-07-13 01:00:00',
...                           '2017-07-13 01:00:00']))
>>> df
                     node   ping mode
2017-07-13 00:30:00    59  116.0   2G
2017-07-13 00:30:00    59    NaN  NaN
2017-07-13 00:30:00    59  106.0   4G
2017-07-13 00:30:00   314   87.0   3G
2017-07-13 01:00:00   314   80.0  NaN
2017-07-13 01:00:00   314    NaN   3G
2017-07-13 01:00:00    59  118.0   2G

>>> def interpolator(series):  # ffill for categoricals, linear otherwise
...     if series.dtype == object:
...         return series.ffill()
...     return series.interpolate()

>>> df.groupby('node').apply(lambda subdf: subdf.apply(interpolator))
---------------------------------------------------------------------------
ValueError: cannot reindex from a duplicate axis

Anything obvious?

Edit: It works, however, if I df.reset_index(inplace=True) before grouping.

[bdforbes]

I never resolved my earlier problem, I managed to get around it by performing my operations along the other axis and transposing. It doesn't appear that your issue fits that pattern however.

[dayuhuang]

I have the same issue here. This seems to have something to do with index of the data frame. Please see the following example to reproduce the error:

data = pd.DataFrame({'x':[1,2,3],'y':[0,1,0]}, index=[0,0,1])
data.groupby(by='y').apply(lambda u: u)

Error: ValueError: cannot reindex from a duplicate axis

However, the following code which only differs by one element in the index will execute without producing the error:

data = pd.DataFrame({'x':[1,2,3],'y':[0,1,0]}, index=[0,1,1])
data.groupby(by='y').apply(lambda u: u)

Note that the only change is the index of the second row.

python 3.5
pandas 0.20.3

[jreback]

I think you can just set the index in this case (this is internally where the error happens), rather than re-index, as its a transformation. PR's welcome.