BUG: DataFrame.sum() changes int columns to float when only int and float columns
Herein, I use int and float as abbrevs. for int64 and float64 dtypes.

In Pandas 0.24.2 running under Debian on Windows Subsytem for Linux, if I have a dataframe, df,  with an int column and I execute:

    df.loc['Totals'] = df.sum()

to add a summation row, then the int column remains of type int, as expected.  But if I instead use:

    df.loc['Totals'] = df.sum(numeric_only=True)

to filter to just numeric columns (which I need to do), then any int column is changed to a float, causing display formatting problems, e.g. when showing row and column numbers.

Is there an easy fix for this?  Thanks.

May be related to issue #13416.
