keepdims fails when taking mean

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

print(np.mean(np.zeros((30, 30)), axis=0, keepdims=True).shape,
pd.DataFrame(np.zeros((30, 30))).mean( axis=0, keepdims=True).shape,
np.mean(pd.DataFrame(np.zeros((30, 30))), axis=0, keepdims=True).shape)

Problem description

The keepdims argument is ignored when taking the mean of pandas dataframes, resulting in an array of lower dimensionality. Oddly, there’s no documentation for pd.DataFrame.mean suggesting keepdims should even be allowed as an argument, but it accepts it with no effect. This also modifies the behavior of np.mean.

Expected Output

(1, 30) (1,30,) (1,30,)

Output of pd.show_versions()

(1, 30) (30,) (30,)

INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-75-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8

pandas: 0.17.1 nose: 1.3.7 pip: None setuptools: None Cython: None numpy: 1.11.0 scipy: 0.17.0 statsmodels: None IPython: 2.4.1 sphinx: None patsy: None dateutil: 2.4.2 pytz: 2014.10 blosc: None bottleneck: None tables: 3.2.2 numexpr: 2.4.3 matplotlib: 1.5.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: 4.4.1 html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None Jinja2: None

[TomAugspurger]

Ok, let’s close for now. We can close for now. We can revisit a keepdims keyword in the future if there’s interest.

[seth-a]

Ah, that makes sense. Nope, looking at the code it only checks that there’s a mean property, not even if it’s a method:

    if type(a) is not mu.ndarray:
        try:
            mean = a.mean
        except AttributeError:
            pass
        else:
            return mean(axis=axis, dtype=dtype, out=out, **kwargs)

Well, definitely not a pandas bug. Thanks for looking at that.