Pandas numpy.std filed in element wise groupby + apply

From SO:

np.random.seed([3,14159])
df = pd.DataFrame(
    np.random.randint(10, size=(3, 3, 5)).tolist(),
    list('XYZ'), list('ABC')
).applymap(np.array)

df.loc['X', 'B'] = np.nan
df.loc['Z', 'A'] = np.nan
print (df)

df2               = df.stack().groupby(level=1)
element_wise_mean = df2.apply(np.mean, axis=0)
print (element_wise_mean)
A                        [4.0, 5.5, 2.5, 1.0, 7.0]
B                        [5.0, 2.5, 7.0, 4.5, 7.0]
C    [5.66666666667, 3.0, 4.0, 4.0, 8.66666666667]

element_wise_sd   = df2.apply(np.std, axis=0)
print (element_wise_sd)

TypeError: setting an array element with a sequence.

I try find solution and if cast to numpy array it working nice.

I know pandas not support arrays using this way very nice, but maybe it is simple bug.

Thanks.

Comment From: jreback

In [13]: df.stack().groupby(level=0).apply(lambda x: np.std(x.tolist()))
Out[13]: 
X    3.168596
Y    2.489087
Z    2.837252
dtype: float64

you need to stress in SO answers that this is a completely non-idiomatic way of doing things, and pandas does not support this, aside from being completely non-performant.