Duplicated rows when merging dataframes in Python

Question

I am currently merging two dataframes with an outer join. However, after merging, I see all the rows are duplicated even when the columns that I merged upon contain the same values.

Specifically, I have the following code.

merged_df = pd.merge(df1, df2, on=['email_address'], how='inner') 

Here are the two dataframes and the results.

df1

          email_address    name   surname 0  [email protected]    john     smith 1  [email protected]    john     smith 2       [email protected]   elvis   presley 

df2

          email_address    street  city 0  [email protected]   street1    NY 1  [email protected]   street1    NY 2       [email protected]   street2    LA 

merged_df

          email_address    name   surname    street  city 0  [email protected]    john     smith   street1    NY 1  [email protected]    john     smith   street1    NY 2  [email protected]    john     smith   street1    NY 3  [email protected]    john     smith   street1    NY 4       [email protected]   elvis   presley   street2    LA 5       [email protected]   elvis   presley   street2    LA 

My question is, shouldn't it be like this?

This is how I would like my merged_df to be like.

          email_address    name   surname    street  city 0  [email protected]    john     smith   street1    NY 1  [email protected]    john     smith   street1    NY 2       [email protected]   elvis   presley   street2    LA 

Are there any ways I can achieve this?

Answer 1

list_2_nodups = list_2.drop_duplicates() pd.merge(list_1 , list_2_nodups , on=['email_address']) 

The duplicate rows are expected. Each john smith in list_1 matches with each john smith in list_2. I had to drop the duplicates in one of the lists. I chose list_2.