[FIXED] Duplicated rows when merging dataframes in Python

Issue

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  john.smith@email.com    john     smith
1  john.smith@email.com    john     smith
2       elvis@email.com   elvis   presley

df2

          email_address    street  city
0  john.smith@email.com   street1    NY
1  john.smith@email.com   street1    NY
2       elvis@email.com   street2    LA

merged_df

          email_address    name   surname    street  city
0  john.smith@email.com    john     smith   street1    NY
1  john.smith@email.com    john     smith   street1    NY
2  john.smith@email.com    john     smith   street1    NY
3  john.smith@email.com    john     smith   street1    NY
4       elvis@email.com   elvis   presley   street2    LA
5       elvis@email.com   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  john.smith@email.com    john     smith   street1    NY
1  john.smith@email.com    john     smith   street1    NY
2       elvis@email.com   elvis   presley   street2    LA

Are there any ways I can achieve this?

Solution

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.

Answered By - piRSquared