How to drop duplicates in one column based on values in 2 other columns in DataFrame in Python Pandas?

Question

I have DataFrame in Python Pandas like below:

data types:

• ID - int

• TYPE - object

• TG_A - int

• TG_B - int

  ID	TYPE	TG_A	TG_B
  111	A	1	0
  111	B	1	0
  222	B	1	0
  222	A	1	0
  333	B	0	1
  333	A	0	1

And I need to drop duplicates in above DataFrame, so as to:

• If value in ID in my DF is duplicated -> drop rows where TYPE = B and TG_A = 1 or TYPE = A and TG_B = 1

So, as a result I need something like below:

ID  | TYPE | TG_A | TG_B
----|------|------|-----
111 | A    | 1    | 0
222 | A    | 1    | 0
333 | B    | 0    | 1

How can I do that in Python Pandas ?

Answer 1

You can use two boolean masks and groupby.idxmax to get the first non matching value:

m1 = df['TYPE'].eq('B') & df['TG_A'].eq(1)
m2 = df['TYPE'].eq('A') & df['TG_B'].eq(1)

out = df.loc[(~(m1|m2)).groupby(df['ID']).idxmax()]

Output:

    ID TYPE  TG_A  TG_B
0  111    A     1     0
3  222    A     1     0
4  333    B     0     1

Answer 2

df[df['TYPE'].eq('A').eq(df['TG_A'])]

result

    ID  TYPE    TG_A    TG_B
0   111 A       1       0
3   222 A       1       0
4   333 B       0       1