How to replace dataframe values based on index statistics

I have a dataframe like this:

l1 = [1,2,3,4,5,6,7,8,9,10]
l2 = [11,12,13,14,15,16,17,18,19,20]
index = ['FORD','GM']
df = pd.DataFrame(l1,l2).reset_index().T
df.index = index

I want to replace these integer values based on this:

For every index, if the value is less than mean-2, then it is 'MINI' else 'MEGA'.

Here the mean varies for every row.

The desired out looks like this:

	0	1	2	3	4	5	6	7	8	9
FORD	MINI	MINI	MINI	MEGA	MEGA	MEGA	MEGA	MEGA	MEGA	MEGA
GM	MINI	MINI	MINI	MEGA	MEGA	MEGA	MEGA	MEGA	MEGA	MEGA

Can anyone help me with this?

Solution

You can compare each row to its mean - 2 and if it is ≤ (lt) assign the MINI/MEGA values with numpy.where:

out = df.copy()

out[:] = np.where(df.lt(df.mean(axis=1).sub(2), axis=0), 'MINI', 'MEGA')

Output:

         0     1     2     3     4     5     6     7     8     9
FORD  MINI  MINI  MINI  MEGA  MEGA  MEGA  MEGA  MEGA  MEGA  MEGA
GM    MINI  MINI  MINI  MEGA  MEGA  MEGA  MEGA  MEGA  MEGA  MEGA