Merge two dataframes where requirements are met

I have two dataframes that I am trying to merge where certain criteria is met.

I want to merge DF2 into DF1 where the Client MAC and AP MAC match but also where the 'Description' contains 'AP' and the 'Client VLAN' is equal to '1234'. I then want to populate the 'AP Interface' with the corresponding 'Interface'.

My actual DF1 is over 10k rows and DF2 is typically 300 or less.

DF1:

DF2:

Desired output:

The code I'm using that is giving me the below output:

def helper(data, col): return data.groupby(col).cumcount()

df1['Client MAC'] = df1['Client MAC'].fillna("")
df2['AP MAC'] = df2['AP MAC'].fillna("")

df = df1.merge(df2, left_on=['Client MAC', helper(df1, ['Client MAC'])], right_on=['AP MAC', helper(df2, \
                        ['AP MAC'])], how='left').drop(columns='key_1')

Current output:

With merge + mask operations:

df = df1.merge(df2, left_on='Client MAC', right_on='AP MAC', how='left')
df['AP Intf'] = (df['AP Intf'].mask(df['Description'].str.contains('AP')
                                    & df['Client VLAN'].eq(1234), df['Interface']))

  Switch Interface Description  Client VLAN      Client MAC AP Name   AP Intf          AP MAC
0   SW1   Gi1/0/1     AP Port          1234  1234.1234.1234     AP1   Gi1/0/1   1234.1234.1234
1   SW1   Gi1/0/1     AP Port          2344  3210.3210.3210     NaN       NaN              NaN
2   SW1   Gi1/0/2     AP Port          1234  3456.3456.3456     AP2   Gi1/0/2   3456.3456.3456
3   SW2   Gi2/0/5    Computer          2343  6543.6543.6543     NaN       NaN              NaN
4   SW2   Gi2/0/8       Trunk          1234  2345.2345.2345     AP3       NaN   2345.2345.2345
5   SW3   Te1/1/1     AP Port          1234  7890.7890.7890     AP4   Te1/1/1   7890.7890.7890
6   SW4   Gi3/0/6     AP Port          1234  2345.2345.2345     AP3   Gi3/0/6   2345.2345.2345