add total row count in dataframe without impacting column values

Build a new dataframe with additional row (retaining the initial columns):

(pd.DataFrame([*df.values, ['Total', *df.sum(numeric_only=True).values]], 
              columns=df.columns))

     Sec  issues  Gaps
0   AAA       22     5
1   BBB       33     2
2   CCC       44     1
3  Total      99     8

This solution worked -- df.loc["Total"] = df.sum()

About your attempt:

• What is your pd.__version__? df.append is deprecated since version 1.4.0. Our answers will not necessarily fit your version. Here is mine: 2.0.3
• The next issue is .sum(numeric_only=True) not having the correct size for insertion, because a non numeric column will be skipped, not written in, as None for example (wish it was).

Alternatively:

Temporarily hiding non numeric columns into the index looks like the simplest alternative and does not suffer from deprecation: (based on Adding sum row to specific columns in dataframe)

df.set_index(  keys='Sec', inplace=True) # Send object column to index
df.loc['TOTAL'] = df.sum()               # Add sum row
df.reset_index(drop=False, inplace=True) # Restore index

Output:

     Sec  issues  Gaps
0    AAA      22     5
1    BBB      33     2
2    CCC      44     1
3  TOTAL      99     8

A note:

• This method avoids the risk of mixing up sums that might arise with other methods involving reconstructing another dataframe.
• Slightly outside the scope of your question, this can be adapted to the case where df were to have several non numeric columns scattered in between numeric columns.