How to return NaN if all values are NaN using the agg() function specifying aggregation output columns

I have a dataframe like so:

data = {'Integers': [1, 2, np.nan, 4, 5],
        'AllNaN': [np.nan, np.nan, np.nan, np.nan, np.nan]}
df = pd.DataFrame(data)

I want to return NaN when performing the sum aggregations on the datagrame. There are solutions on here that advises to use agg(pd.Series.sum, min_count=1). However the way I have my aggregations are using the alternate agg method like so:

agg_df=df.agg(SummedInt=('Integers','sum'), sumofallNaN=('AllNaN','sum')).reset_index()

How do I use the min_count=1 argument with this method?

Answer

IIUC, you can use a lambda function like this:

agg(SummedInt=('Integers',lambda x: x.sum(min_count=1)))

Output:

           Integers
SummedInt      12.0