userID  browser         slot    n_clicks    n_queries   n_nonclk_queries
0   1       Browser #2      exp         23          32      19
1   3       Browser #4      exp         3           4       2
2   5       Browser #4      exp         29          35      16
3   6       Browser #4      control     12          6       0
4   7       Browser #4      exp         54          68      30

exp_clicks     = data[data["slot"] == 'exp'].n_clicks
cntrl_clicks   = data[data["slot"] == 'control'].n_clicks

stats.mannwhitneyu(exp_clicks, cntrl_clicks)

In [221]: df
Out[221]:
   userID      browser     slot  n_clicks  n_queries  n_nonclk_queries
0       1   Browser #2      exp        23         32                19
1       1  Browser #33      exp       100        100               100
2       3   Browser #4      exp         3          4                 2
3       5   Browser #4      exp        29         35                16
4       6   Browser #4  control        12          6                 0

In [222]: df.groupby('userID', as_index=False).sum()
Out[222]:
   userID  n_clicks  n_queries  n_nonclk_queries
0       1       123        132               119
1       3         3          4                 2
2       5        29         35                16
3       6        12          6                 0

In [2]: df
Out[2]:
   userID      browser     slot  n_clicks  n_queries  n_nonclk_queries
0       1   Browser #2      exp        23         32                19
1       1  Browser #22      exp       100        100               100
2       1  Browser #33  control       200        200               200
3       3   Browser #4      exp         3          4                 2
4       5   Browser #4      exp        29         35                16
5       6   Browser #4  control        12          6                 0

In [5]: df.groupby(['userID','slot'], as_index=False).sum()
Out[5]:
   userID     slot  n_clicks  n_queries  n_nonclk_queries
0       1  control       200        200               200
1       1      exp       123        132               119
2       3      exp         3          4                 2
3       5      exp        29         35                16
4       6  control        12          6                 0

In [24]: funcs = {c:'sum' for c in df.select_dtypes(include='number').drop('userID',1).columns}

In [25]: funcs
Out[25]: {'n_clicks': 'sum', 'n_queries': 'sum', 'n_nonclk_queries': 'sum'}

In [26]: funcs['slot'] = lambda x: x.values.tolist()

In [27]: df.groupby('userID', as_index=False).agg(funcs)
Out[27]:
   userID  n_clicks  n_queries  n_nonclk_queries                 slot
0       1       323        332               319  [exp, exp, control]
1       3         3          4                 2                [exp]
2       5        29         35                16                [exp]
3       6        12          6                 0            [control]

In [28]: funcs['slot'] = 'first'

In [29]: funcs
Out[29]:
{'n_clicks': 'sum',
 'n_queries': 'sum',
 'n_nonclk_queries': 'sum',
 'slot': 'first'}

In [30]: df.groupby('userID', as_index=False).agg(funcs)
Out[30]:
   userID  n_clicks  n_queries  n_nonclk_queries     slot
0       1       323        332               319      exp
1       3         3          4                 2      exp
2       5        29         35                16      exp
3       6        12          6                 0  control