Duplicated rows when merging dataframes in Python

Question

I am currently merging two dataframes with an inner 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?

Answer 1

To make sure you don't have duplicates in your keys, you can use the validate parameter:

validate : str, optional

If specified, checks if merge is of specified type.

• “one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
• “one_to_many” or “1:m”: check if merge keys are unique in left dataset.
• “many_to_one” or “m:1”: check if merge keys are unique in right dataset.
• “many_to_many” or “m:m”: allowed, but does not result in checks.

In your case, you don't want any duplicate keys in the "right" dataframe df2, so you need to set validate to many_to_one.

df1.merge(df2, on=['email_address'], validate='many_to_one')

If you have duplicate keys in df2, the function will return this error:

MergeError: Merge keys are not unique in right record; not a many-to-one merge

To drop duplicate keys in df2 and do a merge you can use:

keys = ['email_address']
df1.merge(df2.drop_duplicates(subset=keys), on=keys)

Make sure you set the subset parameter in drop_duplicates to the key columns you are using to merge. If you don't specify a subset drop_duplicates will compare all columns and if some of them have different values it will not drop those rows.

Answer 2

DO NOT drop duplicates BEFORE the merge, but after!

Best solution is do the merge and then drop the duplicates.

In your case:

merged_df = pd.merge(df1, df2, on=['email_address'], how='inner')
merged_df.drop_duplicates(subset=['email_address'], keep='first', inplace=True, ignore_index=True)

Answer 3

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.