根据来自另一个数据框的行中的匹配值排除数据框中的行

Question

I have two dataframes (A and B). I want to remove all the rows in B where the values for columns Month, Year, Type, Name are an exact match.

Dataframe A

   Name    Type   Month   Year  country Amount   Expiration  Paid
0 EXTRON   GOLD   March   2019    CA    20000   2019-09-07   yes
0 LEAF    SILVER  March   2019    PL    4893    2019-02-02   yes       
0 JMC     GOLD    March   2019    IN    7000    2020-01-16   no       

Dataframe B

  Name     Type   Month   Year  country Amount   Expiration  Paid
0 JONS    GOLD    March   2018    PL    500     2019-10-17   yes
0 ABBY    BRONZE  March   2019    AU    60000   2019-02-02   yes       
0 BUYT     GOLD   March   2018    BR     50     2018-03-22   no       
0 EXTRON  GOLD    March   2019    CA    90000   2019-09-07   yes
0 JAYB    PURPLE  March   2019    PL    9.90    2018-04-20   yes       
0 JMC     GOLD    March   2019    IN    6000    2020-01-16   no       
0 JMC     GOLD    April   2019    IN    1000    2020-01-16   no      

Desired Output:

Dataframe B

  Name       Type   Month   Year  country Amount   Expiration  Paid
0 JONS    GOLD    March   2018    PL    500     2019-10-17   yes
0 ABBY    BRONZE  March   2019    AU    60000   2019-02-02   yes       
0 BUYT     GOLD   March   2018    BR     50     2018-03-22   no       
0 JAYB    PURPLE  March   2019    PL    9.90    2018-04-20   yes       
0 JMC     GOLD    April   2019    IN    1000    2020-01-16   no

Answer 1

We can using merge here

l=['Month', 'Year','Type', 'Name']
B=B.merge(A[l],on=l,indicator=True,how='outer').loc[lambda x : x['_merge']=='left_only'].copy() 
# you can add drop here like B=B.drop('_merge',1)
   Name    Type  Month  Year country   Amount  Expiration Paid     _merge
0  JONS    GOLD  March  2018      PL    500.0  2019-10-17  yes  left_only
1  ABBY  BRONZE  March  2019      AU  60000.0  2019-02-02  yes  left_only
2  BUYT    GOLD  March  2018      BR     50.0  2018-03-22   no  left_only
4  JAYB  PURPLE  March  2019      PL      9.9  2018-04-20  yes  left_only
6   JMC    GOLD  April  2019      IN   1000.0  2020-01-16   no  left_only

Answer 2

I tried using MultiIndex for the same.

cols =['Month', 'Year','Type', 'Name']
index1 = pd.MultiIndex.from_arrays([df1[col] for col in cols])
index2 = pd.MultiIndex.from_arrays([df2[col] for col in cols])
df2 = df2.loc[~index2.isin(index1)]