Grouping and merging with pandas - python-3.x

Question

I need some help with this.
I´ve to transform this dataframe without duplicates in column "name"
you can see that I have duplicates in column "name" ex:John , Joan
df0 = pd.DataFrame({'name':['John','John','Joan','Joan','Juan'],
'time':[20,10,11,18, 15],
'amount':[100, 400, 200, 100, 300]})
df0
name time amount
0 John 20 100
1 John 10 400
2 Joan 11 200
3 Joan 18 100
4 Juan 15 300
I need to transform this, grouping the dataframe in this way, I don´t know if is the right way.
dfend0 = df0.groupby('name').agg(lambda x: x.tolist())
dfend0
time amount
name
Joan [11, 18] [200, 100]
John [20, 10] [100, 400]
Juan [15] [300]
The column "name" is now the index, this isn´t the behavior I was looking for
list(dfend0.columns.values)
['time', 'amount']
#Now I need to merge with other dataframe
df1 = pd.DataFrame({
'name' : ['John' ,'Joan', 'Juan'],
'address' : ['streetA','streetB','streetC'],
'age' : [30,40,50]
})
df1
name address age
0 John streetA 30
1 Joan streetB 40
2 Juan streetC 50
ender = df1.merge(df0)
ender
name address age time amount
0 John streetA 30 20 100
1 John streetA 30 10 400
2 Joan streetB 40 11 200
3 Joan streetB 40 18 100
4 Juan streetC 50 15 300
This is not what I´m looking for, this example would be more accurate:
name address age time amount
0 John streetA 30 20,10 100,400
1 Joan streetB 40 11,18 200,100
2 Juan streetC 50 15 300
Any clue?

Answer 1

First, use as_index=False if you don't want the name as the index after the groupby operation.
2nd, there is no need for the lambda use .agg(list)
dfend0 = df0.groupby('name',as_index=False).agg(list)
then merge as usual.
df2 = pd.merge(df1,df0end,on='name')
name address age time amount
0 John streetA 30 [20, 10] [100, 400]
1 Joan streetB 40 [11, 18] [200, 100]
2 Juan streetC 50 [15] [300]
Note, if you dont want lists use (not recommended as you lost the underlying datatype and end up with a string)
df0end = df0.astype(str).groupby('name',as_index=False).agg(','.join)
name time amount
0 Joan 11,18 200,100
1 John 20,10 100,400
2 Juan 15 300
df2 = pd.merge(df1,df0end,on='name')
name address age time amount
0 John streetA 30 20,10 100,400
1 Joan streetB 40 11,18 200,100
2 Juan streetC 50 15 300

Answer 2

df = pd.DataFrame({'name':['John','John','Joan','Joan','Juan'],
'time':[20,10,11,18, 15],
'amount':[100, 400, 200, 100, 300]})
df=df.astype(str).groupby('name').agg({
'time':lambda x : ','.join(x),
'amount':lambda x : ','.join(x)
})
print(df)
time amount
name
Joan 11,18 200,100
John 20,10 100,400
Juan 15 300
at the end use df=df.merge(df2,on='name')