Mapping key to multiple values in a dictionary pandas

Question

I have two data frames. The first is a list of email addresses

email_ad                   Band 30     Band 40
example_email@gmail.com
sample_email@gmail.com

The second is the following data frame

email_ad                 Name       Manager_Name   Manager_Band_level 
example_email@gmail.com. Tom Banks  Boss1          30
sample_email@gmail.com.  Bill Bob   Boss2          40

I want to map each email in the first dataframe to the second, if the email is a match, then check the Manager Band level. Based off the number, fill that corresponding manager's email into the "Band 30" or "Band 40" categories.

So the desired dataframe is the following:

email_ad                   Band 30     Band 40
example_email@gmail.com    Boss1
sample_email@gmail.com.                Boss2

if someone could please help me for syntax that would be amazing. I was additionally stuck making a dictionary out of only certain columns in the dataframe. Thanks so much!!

Answer 1

You can do this with a pivot.

pivoted = df.pivot('email_ad', 'Manager_Band_level', 'Manager_Name')
pivoted
# Manager_Band_level          30     40
# email_ad                             
# example_email@gmail.com  Boss1    NaN
# sample_email@gmail.com     NaN  Boss2

That format is probably usable as is. But if you want to clean it up a bit more, you can fill in the missing values and flatten it back out.

cleaned = pivoted.fillna('').reset_index()
cleaned
# Manager_Band_level                 email_ad     30     40
# 0                   example_email@gmail.com  Boss1       
# 1                    sample_email@gmail.com         Boss2

You can get rid of the awkward, and now incorrect, column index name by resetting the columns .

cleaned.columns = list(cleaned.columns)
cleaned
#                   email_ad     30     40
# 0  example_email@gmail.com  Boss1       
# 1   sample_email@gmail.com         Boss2

If the Band prefix is important, you can add that at the same time.

cleaned.columns = [col if col == 'email_ad' else f'Band {col}' for col in cleaned.columns] 
cleaned
#                   email_ad Band 30 Band 40
# 0  example_email@gmail.com   Boss1        
# 1   sample_email@gmail.com           Boss2

Answer 2

Try using merge and maybe a loop if there are multiple bands:

import pandas as pd
from io import StringIO

s1 = '''
email_ad,Band 30,Band 40
example_email@gmail.com,,
sample_email@gmail.com,,
'''

s2= '''
email_ad,Name,Manager_Name,Manager_Band_level
example_email@gmail.com,Tom Banks,Boss1,30
sample_email@gmail.com,Bill Bob,Boss2,40
'''


df1 = pd.read_csv(StringIO(s1))
df2 = pd.read_csv(StringIO(s2))

And then do the merge

df3 = df1.merge(df2, how='left', left_on='email_ad', right_on='email_ad')
for i in (30,40):
    df3.loc[df3.Manager_Band_level == i, f'Band {i}'] = \
        df3.loc[df3.Manager_Band_level == i, 'Manager_Name']

df3[['email_ad', 'Band 30', 'Band 40']]

#       email_ad    Band 30 Band 40
# 0 example_email@gmail.com Boss1   NaN
# 1 sample_email@gmail.com  NaN Boss2