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

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

[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.

[rhshadrach]

@pwwang - mutating a DataFrame in apply is not supported, see here:

https://pandas.pydata.org/docs/user_guide/gotchas.html#mutating-with-user-defined-function-udf-methods