how to use pandas isin for multiple columns

Question

I want to find the values of col1 and col2 where the col1 and col2 of the first dataframe are

Answer 1

If somehow you must stick to isin or the negate version ~isin. You may first create a new column, with the concatenation of col1, col2. Then use isin to filter your data. Here is the code:

import pandas as pd
df1 = pd.DataFrame({'col1': ['pizza', 'hamburger', 'hamburger', 'pizza', 'ice cream'], 'col2': ['boy', 'boy', 'girl', 'girl', 'boy']}, index=range(1,6))
df2 = pd.DataFrame({'col1': ['pizza', 'pizza', 'chicken', 'cake', 'cake', 'chicken', 'ice cream'], 'col2': ['boy', 'girl', 'girl', 'boy', 'girl', 'boy', 'boy']}, index=range(10,17))

df1['indicator'] = df1['col1'].str.cat(df1['col2'])
df2['indicator'] = df2['col1'].str.cat(df2['col2'])

df2.loc[df2['indicator'].isin(df1['indicator'])].drop(columns=['indicator'])

which gives

    col1    col2
10  pizza   boy
11  pizza   girl
16  ice cream   boy

If you do so remember to make sure that concatenating two columns doesn't create false positives e.g. concatenation of 123 and 456 in df1 and concatenation of 12 and 3456 in df2 will match even though their respective columns don't match. You can fix this problem by additional sep parameter.

df1['indicator'] = df1['col1'].str.cat(df1['col2'], sep='$$$')
df2['indicator'] = df2['col1'].str.cat(df2['col2'], sep='$$$')