BUG: `df.agg` raises Exception on valid function in `df.apply`

[attack68]

Pandas version checks

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Reproducible Example

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Issue Description

If the following agg is performed it currently works but gives a warning:

df = pd.DataFrame({
    "A": Series((1000, 2000), dtype=int),
    "B": Series((1000, 2000), dtype=np.int64),
    "C": Series(["a", "b"]),
})

df.agg(["mean", "sum"])
           A       B    C
mean  1500.0  1500.0  NaN
sum   3000.0  3000.0   ab

FutureWarning: ['C'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning. print(df.agg(["mean", "sum"]))

However, I do not want to:

• drop column 'C', because there is at least one op, sum, which produces a valid results for that column.
• drop op 'mean' because there is at least one column, 'A', 'B', which produce valid results for that op.

I tried to design a function which would error trap this:

def mean2(s:Series):
    try:
        ret = s.mean()
    except Exception:
        ret = pd.NA
    return ret

df.agg([mean2, "sum"])
<ValueError: cannot combine transform and aggregation operations>

Oddly, this works with apply which is what the agg docs give guidance on:

df.apply(mean2, axis=0)
A    1500.0
B    1500.0
C      <NA>
dtype: object

So what's the solution here?

Expected Behavior

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Installed Versions

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[Jaafarben2]

Seems that in this circumstance (in list argument to pandas.DataFrame.aggregate), pandas first tries to apply the aggregating function to each data point, and from the moment this fails, falls back to the correct behaviour (calling back with the Series to be aggregated).

source : https://stackoverflow.com/questions/54890646/pandas-fails-to-aggregate-with-a-list-of-aggregation-functions

The solution is to force Series arguments:

def mean2(s:Series):
    if not isinstance(s,Series):
        raise ValueError('need Series argument')
    try:
        ret = s.mean()
    except Exception:
        ret = pd.NA
    return ret

df.agg([mean2, "sum"])
            A       B     C
mean2  1500.0  1500.0  <NA>
sum    3000.0  3000.0    ab