Pandas: Multiple columns containing Names and Values of Variables: How to use Pivot?

i am not sure if, and how i can do the following transformation:

I have a DataFrame looking like this:

Index   Name    detail1 detail1_value   detail2  detail2_value   detail3    detail3_value
1     Albert    Age      30             Group       A            Hometown   beautifulplace
2     Bea       Age      28             Hometown    anotherplace None       None
3     Celin     Age      45             Group       B            None       None
4     Dave      Group    A              None        None         None       None

But as you can imagine, my aim is:

Index   Name    Age Group   Hometown
1     Albert    30  A   beautifulplace
2     Bea       28      anotherplace
3     Celin     45  B   
4     Dave          A   

I am pretty sure that ech detail just appears once. To keep things complex: i am not sure if every detail is total identical (in some cases for example Hometowns instead of Hometown).

The only solution i can see so far is to produce singel pivot-tables out of each pair of columns (like detail1 and detail1_value). In a second step an new dataset is created and each of these pivot-tables is searched for example on the Information on the Age. But my trust in python tells me, that there must be a better way...

Thanks!

PS: May help:

dataset = pd.DataFrame({'Name': ['Albert', 'Bea', 'Celine', 'Dave'],
                        'detail1': ['Age', 'Age', 'Age', 'Group'],
                        'detail1_value': ['30', '28', '45', 'A'],
                        'detail2': ['Group', 'Hometown', 'Group', None],
                        'detail2_value': ['A', 'anotherplace', 'B', None],
                        'detail3': ['Hometown', None, None, None],
                        'detail3_value': ['beautifulplace', None, None, None]})

You can use lreshape with pivot:

#get columns names dynamically 
a = dataset.columns[dataset.columns.str.endswith('_value')]
b = dataset.columns[dataset.columns.str.startswith('detail')].difference(a)

df = pd.lreshape(dataset, {'detail':b, 'value':a})
print (df)
     Name           value    detail
0  Albert              30       Age
1     Bea              28       Age
2  Celine              45       Age
3    Dave               A     Group
4  Albert               A     Group
5     Bea    anotherplace  Hometown
6  Celine               B     Group
7  Albert  beautifulplace  Hometown


df = df.pivot(index='Name', columns='detail', values='value')
print (df)
detail   Age Group        Hometown
Name                              
Albert    30     A  beautifulplace
Bea       28  None    anotherplace
Celine    45     B            None
Dave    None     A            None

Some data cleaning last:

df = df.reset_index().rename_axis(None, axis=1)
print (df)
     Name   Age Group        Hometown
0  Albert    30     A  beautifulplace
1     Bea    28  None    anotherplace
2  Celine    45     B            None
3    Dave  None     A            None