Compare Multiple Columns to Get Rows that are Different in Two Pandas Dataframes

Question

I have two dataframes:

df1=
    A    B   C
0   A0   B0  C0
1   A1   B1  C1
2   A2   B2  C2

df2=
    A    B   C
0   A2   B2  C10
1   A1   B3  C11
2   A9   B4         


        

Answer 1

Method ( 1 )

---

In [63]:
df1['A'].isin(df2['A']) & df1['B'].isin(df2['B'])
Out[63]:

0   False
1   False
2   True

Method ( 2 )

---

you can use the left merge to obtain values that exist in both frames + values that exist in the first data frame only

In [10]:
left = pd.merge(df1 , df2 , on = ['A' , 'B'] ,how = 'left')
left
Out[10]:
    A   B   C_x C_y
0   A0  B0  C0  NaN
1   A1  B1  C1  NaN
2   A2  B2  C2  C10

then of course values that exist only in the first frame will have NAN values in columns of the other data frame , then you can filter by this NAN values by doing the following

In [16]:
left.loc[pd.isnull(left['C_y']) , 'A':'C_x']
Out[16]:
    A   B   C_x
0   A0  B0  C0
1   A1  B1  C1

In [17]:

if you want to get whether the values in A exists in B you can do the following

In [20]:
pd.notnull(left['C_y'])
Out[20]:
0    False
1    False
2     True
Name: C_y, dtype: bool