Comparing 2 columns of two Python Pandas dataframes and getting the common rows

Question

I have 2 Dataframe as follows:

DF1=
    A    B   C    D
0   AA   BA  KK   0
1   AD   BD  LL   0
2   AF   BF  MM   0

DF2=
    K    L
0   AA   BA
1   AD   BF
         


        

Answer 1

df1['ColumnName'].isin(df2['ColumnName']).value_counts()

Answer 2

This would be easier if you renamed the columns of df2 and then you can compare row-wise:

In [35]:

df2.columns = ['A', 'B']
df2
Out[35]:
    A   B
0  AA  BA
1  AD  BF
2  AF  BF
In [38]:

df1['D'] = (df1[['A', 'B']] == df2).all(axis=1).astype(int)
df1
Out[38]:
    A   B   C  D
0  AA  BA  KK  1
1  AD  BD  LL  0
2  AF  BF  MM  1

Answer 3

DF1.merge(right=DF2,left_on=[DF1.A,DF1.B],right_on=[DF2.K,DF2.L], indicator=True, how='left')

gives:

A B C D K L _merge 0 AA BA KK 0 AA BA both 1 AD BD LL 0 NaN NaN left_only 2 AF BF MM 0 AF BF both

So, as above, indicator does the job.

Peter

Answer 4

This is how I solved it:

df1 = pd.DataFrame({"A":['AA','AD','AD'], "B":['BA','BD','BF']})
df2 = pd.DataFrame({"A":['AA','AD'], 'B':['BA','BF']})
df1['compressed']=df1.apply(lambda x:'%s%s' % (x['A'],x['B']),axis=1)
df2['compressed']=df2.apply(lambda x:'%s%s' % (x['A'],x['B']),axis=1)
df1['Success'] = df1['compressed'].isin(df2['compressed']).astype(int)
print df1

    A   B     compressed   Success
0  AA  BA      AABA          1
1  AD  BD      ADBD          0
2  AD  BF      ADBF          1