If you have just a few columns to sum, you can write:

df['e'] = df['a'] + df['b'] + df['d']

This creates new column e with the values:

   a  b   c  d   e
0  1  2  dd  5   8
1  2  3  ee  9  14
2  3  4  ff  1   8

For longer lists of columns, EdChum's answer is preferred.

append and assign

The point of this answer is to provide an in line and not an in place solution.

append

I use append to stack a Series or DataFrame vertically. It also creates a copy so that I can continue to chain.

assign

I use assign to add a column. However, the DataFrame I'm working on is in the in between nether space. So I use a lambda in the assign argument which tells Pandas to apply it to the calling DataFrame.

df.append(df.sum().rename('Total')).assign(Total=lambda d: d.sum(1))

              Graduate  Undergraduate  Total
Not                440           3760   4200
Straight A's        60            240    300
Total              500           4000   4500

Fun alternative

Uses drop with errors='ignore' to get rid of potentially pre-existing Total rows and columns.

Also, still in line.

def tc(d):
  return d.assign(Total=d.drop('Total', errors='ignore', axis=1).sum(1))

df.pipe(tc).T.pipe(tc).T

              Graduate  Undergraduate  Total
Not                440           3760   4200
Straight A's        60            240    300
Total              500           4000   4500