Pandas - Updating columns based on several conditions - group by method

I am aiming to the below output.

A	B	C	D	E	F
0000	ZZZ	987	QW1	8	first three-four col and offset
0000	ZZZ	987	QW1	-8	first three-four col and offset
0000	ZZZ	987	QW1	-8	first or no match
1111	AAA	123	AB1	1	first three-four col and offset
1111	AAA	123	CD1	-1	first three-four col and offset
2222	BBB	456	EF1	-4	first three-four col and offset
2222	BBB	456	GH1	-1	first three-four col and offset
2222	BBB	456	IL1	5	first three-four col and offset
3333	CCC	789	MN1	2	first two col and offset
3333	CCC	101	MN1	-2	first two col and offset
4444	DDD	121	UYT	6	first two col and offset
4444	DDD	131	FB1	-5	first two col and offset
4444	DDD	141	UYT	-1	first two col and offset
5555	EEE	151	CB1	3	first two col and offset
5555	EEE	161	CR1	-3	first two col and offset
5555	EEE	161	CR1	-5	first or no match
6666	FFF	111	CB1	4	first or no match
7777	GGG	222	ZB1	10.5	first three-four col and small offset
7777	GGG	222	ZB1	-10	first three-four col and small offset

1st rule) the first three columns must equal each other - regardless of the fourth, which can either be equal or not. Each combination must have the associated numbers (col E) offset to zero (can be from 2 to X records combined). IT SHOULD WORK EVEN WITH UNEVEN ENTRIES.

2nd rule) the first two columns must equal each other - regardless of the fourth, which can either be equal or not. Each combination must have the associated numbers (col E) offset to zero (can be from 2 to X records combined). IT SHOULD WORK EVEN WITH UNEVEN ENTRIES.

3rd rule) no match.

4th rule) the first three columns must equal each other - regardless of the fourth, which can either be equal or not. Each combination can have a difference of 0.5 AT MOST (col E) and NO offset to zero (can be from 2 to X records combined). IT SHOULD WORK EVEN WITH UNEVEN ENTRIES.

Please see my code below.

Through the below code, I am able to get a similar result, however, this way does not work with uneven entries, for instance the first three rows would result as following:

A	B	C	D	E	F
0000	ZZZ	987	QW1	8	first or no match
0000	ZZZ	987	QW1	-8	first or no match
0000	ZZZ	987	QW1	-8	first or no match

Instead of the following:

A	B	C	D	E	F
0000	ZZZ	987	QW1	8	first three-four col and offset
0000	ZZZ	987	QW1	-8	first three-four col and offset
0000	ZZZ	987	QW1	-8	first or no match

Code so far:

m1 = df.groupby(['A', 'B', 'C'])['E'].transform('sum').eq(0)  # Rule 1
m2 = df.groupby(['A', 'B'])['E'].transform('sum').eq(0)  # Rule 2
m3 = df.groupby(['A', 'B', 'C'])['E'].transform('sum').abs().le(0.5)  # Rule 4

df['new'] = np.select([m1, m2, m3], ['first three-four col and offset',
                      'first two col and offset', 'first three-four col and small offset'], 'first or no match')

from Pandas - Updating columns based on several conditions - group by method