Compare columns of two different dataframes row-wise

I have two dataframes as follows:

df1 =

     col_1   val_1
0    4.0     0.89
1    4.0     0.56
2    49.0    0.7
3    49.0    1.23
4    52.0    0.8

df2 =
     col_2   val_2
0    3.0     9
1    4.0     23.0
2    49.0    85
3    50.0    34
4    52.0    75

I want to compare the columns col_1 of the dataframe df1 and col_2 of the dataframe df2 row-wise and see if there is a match which is indicated by True or False.

for example:

df1 =

     col_1   val_1  Match
0    4.0     0.89   False
1    4.0     0.56   True
2    49.0    0.7    True
3    49.0    1.23   False
4    52.0    0.8    True

I tried the following but it did not give me the desired results:

df1['Match'] = df1.apply(lambda row: all(i in df2.col_2for i in df1.col_1), axis=1)

Is there a way to do this?

CodePudding user response：

Check each row.
Boolean will be assigned in the new column match

df1['Match'] = df1['col_1'] == df2['col_2']

Output:

   col_1  val_1  Match
0    4.0   0.89  False
1    4.0   0.56   True
2   49.0   0.70   True
3   49.0   1.23  False
4   52.0   0.80   True

CodePudding user response：

df1 = pd.DataFrame({'col_1':[4.0, 4.0, 49.0, 49.0, 52.0],
                    'val_1':[0.89, 0.56, 0.7, 1.23, 0.8]})
df2 = pd.DataFrame({'col_2':[3.0, 4.0, 49.0, 50.0, 52.0],
                    'val_2':[9, 23.0, 85, 34, 75]})

df1['Match'] = df1['col_1'] == df2['col_2']