Creating rule function using pandas dataframe;

I have a dataframe in this format;

rule_frame = pd.DataFrame({'PA_1' : ['A','B','A'],
                           'PA_2' : ['Low','Low','Low'],
                           'LA_1' : ['A','B','E'],
                           'LA_2' : ['Low','Low']})

and I want to create a function that is automatically created by this table as an input. The function basically takes every single row as rules and return true, if not it should return false.

What i mean by "automatically created function" should be like;

def i_am_automatically_created(PA_1,PA_2,LA_1,LA_2):
    
    if PA_1 == 'A' and PA_2 == 'Low' and LA_1 == 'A' and LA_2 == 'Low':
        return True
    elif PA_1 == 'B' and PA_2 == 'Low' and LA_1 == 'B' and LA_2 == 'Low':
        return True
    elif PA_1 == 'A' and PA_2 == 'Low' and LA_1 == 'E' and LA_2 == 'High':
        return True
    else:
        return False

The reason why I want to do this flexible is that column and row sizes can differ. I hope I was able to explain.

Thanks in advance.

>Solution :

You can try:

def create_func(df):
    all_funcs = []
    for _, r in df.iterrows():
        all_funcs.append(lambda *x, rules=r: all(r == v for v, r in zip(x, rules)))
    return lambda *x: any(f(*x) for f in all_funcs)

fn = create_func(rule_frame)

# this should pass:
assert fn('A', 'Low', 'A', 'Low') == True
assert fn('B', 'Low', 'B', 'Low') == True
assert fn('A', 'Low', 'A', 'Low') == True
assert fn('A', 'Low', 'E', 'High') == True

# this shouldn't pass:
assert fn('A', 'High', 'E', 'High') == False
assert fn('A', 'High', 'B', 'High') == False

Initial df:

  PA_1 PA_2 LA_1  LA_2
0    A  Low    A   Low
1    B  Low    B   Low
2    A  Low    E  High