How can I handle "Reindexing only valid with uniquely valued Index objects"

I have a dataframe of names (df):

df:

  Group  Name
0     A   jim
1     B   bob
2     C   joe
3     A  jane
4     C   doe

And a schedule dataframe (df1):

df1:

      Group     Due_Date      Assignment
0     A         1-19 17:00    Quiz #1 A
1     B         1-21 16:30    Quiz #1 A
2     C         1-21 16:30    Quiz #1 A
5     A         1-24 11:30    Exam A
6     B         1-24 11:30    Exam A
7     C         1-24 12:30    Exam A

I pivoted df1 (df1_pivoted) to put assignments in the df1 columns:

df1 = df1.pivot(index='Group',
         columns='Assignment',
         values='Due_Date')
df1_pivoted:

Assignment      Exam A   Quiz #1 A
Group
A           1-24 11:30  1-19 17:00
B           1-24 11:30  1-21 16:30
C           1-24 12:30  1-21 16:30

I then try to add the names back in, but I can not figure out how to deal with the "Reindexing only valid with uniquely valued Index objects" error. I presume it is because some names are associated with more than one group:

d=df.set_index('Group')['Name']
p='({})'.format('\\b|\\b'.join(df['Group']))
df3['Name']=df3['Group'].str.extract(p,expand=False).map(d)

results in error: "Reindexing only valid with uniquely valued Index objects"

I want the final dataframe (df1_final) returned as below:

df1_final:
Group   Name    Exam A      Quiz #1 A
A       jim     1-24 11:30  1-19 17:00
B       bob     1-24 11:30  1-21 16:30
C       joe     1-24 12:30  1-21 16:30
A       jane    1-24 11:30  1-19 17:00
C       doe     1-24 12:30  1-21 16:30

If there is a more pythonic approach to going about this, I am definitely up for learning cleaner programming. Thank you for your help. This community is truly awesome. I am new to programing this past year as a hobby, to make work easier/more fun, and I don't know if I would stick with it without all of you and your willingness to help. Thanks again

Solution

The problem is that pandas doesn't know how to handle the direct assignment because A and C have more then one value. Use a left-join in stead to get this to work. Your example is broken (it's missing parts to reproduce), so imagine that df1_pivoted is your pivoted df1 and df3 is created to look like

df3:
Group   Name  
A       jim 
B       bob
C       joe
A       jane 
C       doe

Then the following should do the job

df3.set_index('Group', inplace=True)
df1_pivoted = df1_pivoted.join(df3, how='left')

Alternatively, you can reset the index, and do a merge on the Group columns for the same effect.