“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:

image

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

Author: Fantashit

1 thought on ““cannot reindex from a duplicate axis” when groupby().apply() on MultiIndex columns

  1. 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.

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