Pivot table using Python Pandas, unit price an sum

Main idea is to use pandas...pivot_table().

If you just want to sum your data, then np.sum will do:

>>> df.pivot_table(cols='id', values='credit', rows='product', aggfunc=np.sum)
id        1   2   15
product             
book     -10  -5 NaN
car     -150 NaN NaN
glass    NaN  -4  -2
ipad     -15 NaN NaN
server   -25 NaN -25

Or you can use collections.Counter to get data in format close to your needs (Counter is not very performant, so be careful about this one):

>>> from collections import Counter
>>> df.pivot_table(cols='id', values='credit', rows='product', aggfunc=Counter)
id              1        2         15
product                              
book       {-5: 2}  {-5: 1}       NaN
car      {-150: 1}      NaN       NaN
glass          NaN  {-2: 2}   {-2: 1}
ipad      {-15: 1}      NaN       NaN
server    {-25: 1}      NaN  {-25: 1}

Or define custom function to get exactly what you need:

>>> from collections import defaultdict
>>> def hlp_count(x):
...     d = defaultdict(int)
...     for v in x:
...         d[v] += 1
...     # join in case you have more than one distinct price
...     return ', '.join(['{0} ({1})'.format(k, v) for k, v in d.iteritems()])

>>> df.pivot_table(cols='id', values='credit', rows='product', aggfunc=hlp_count)
id             1       2        15
product                           
book       -5 (2)  -5 (1)      NaN
car      -150 (1)     NaN      NaN
glass         NaN  -2 (2)   -2 (1)
ipad      -15 (1)     NaN      NaN
server    -25 (1)     NaN  -25 (1)