Rearrange table using pandas

Question

I have a table with customer IDs and emails. Some users have multiple emails. The table looks something like this:

| Customer  | Email          |
| ----------| -------------- |
| 1         | jdoe@mail.com  |
| 2         | jane1@mail.com |
| 3         | adam@mail.com  |
| 1         | john_d@mail.com|

What I would like to do is rearrange the table such that each customer ID has only one row and secondary emails are added as additional columns. Something like this:

| Customer  | Email1         |Email2         |
| ----------| -------------- |---------------|
| 1         | jdoe@mail.com  |john_d@mail.com
| 2         | jane1@mail.com |               |
| 3         | adam@mail.com  |               |

What's the best way to do this using pandas? I have tried using df.pivot but that doesn't seem to be working for me.

Answer 1

You can use Series.duplicated() + pd.merge() + DataFrame.drop_duplicates()

# We get the Customers with more than one email.
df_seconds_email = df[df['Customer'].duplicated()]

# We merge your original dataframe (I called it 'df') and the above one, suffixes param help us to get
# 'Email2' column, finally we drop duplicates taking into account 'Customer' column.
df = pd.merge(df, df_seconds_email, how='left', on=['Customer'], suffixes=('', '2')).drop_duplicates(subset='Customer')
print(df)

Output:

    Customer    Email          Email2
0      1    jdoe@mail.com   john_d@mail.com
1      2    jane1@mail.com      NaN
2      3    adam@mail.com       NaN

Answer 2

You can use cumcount to create MultiIndex. Then reshape the data by using unstack and add change columns names by add_prefix :

    df = (df.set_index(['Customer',df.groupby('Customer').cumcount()])['Email']
        .unstack()
        .add_prefix('Email')
        .reset_index())
    print(df)

And you'll get exactly what you want.