TypeError: unhashable type: 'dict' when using apply/transform?

Hello!

I am quite puzzled by some inconsistencies when using apply. Consider this simple example

idx=[pd.to_datetime('2012-02-01 14:00:00') , 
     pd.to_datetime('2012-02-01 14:01:00'),
     pd.to_datetime('2012-03-05 14:04:00'),
     pd.to_datetime('2012-03-05 14:01:00'),
     pd.to_datetime('2012-03-10 14:02:00'),
     pd.to_datetime('2012-03-11 14:07:50')
     ]

test=pd.DataFrame({'value1':[1,2,3,4,5,6],
                   'value2':[10,20,30,40,50,60],
                   'groups' : ['A','A','A','B','B','B']},
    index=idx)

test
Out[22]: 
                    groups  value1  value2
2012-02-01 14:00:00      A       1      10
2012-02-01 14:01:00      A       2      20
2012-03-05 14:04:00      A       3      30
2012-03-05 14:01:00      B       4      40
2012-03-10 14:02:00      B       5      50
2012-03-11 14:07:50      B       6      60

Now, this WORKS

test.groupby('groups').apply(lambda x: x.resample('1 T', label='left', closed='left').apply(
        {'value1' : 'mean',
         'value2' : 'mean'}))

but this FAILS

test.groupby('groups').apply(
        {'value1' : 'mean',
         'value2' : 'mean'})

Traceback (most recent call last):

  File "<ipython-input-24-741304ecf105>", line 3, in <module>
    'value2' : 'mean'})

  File "C:\Users\\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\groupby.py", line 696, in apply
    func = self._is_builtin_func(func)

  File "C:\Users\\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\base.py", line 730, in _is_builtin_func
    return self._builtin_table.get(arg, arg)

TypeError: unhashable type: 'dict'

This worked in prior versions of Pandas. What is the new syntax then? Some very useful variant of the code above I used to use was:

test.groupby('groups').apply(
        {'newname1' : {'value1' : 'mean'},
         'newname2' : {'value2' : 'mean'}})

to rename the new variables on the fly. Is this still possible now? Is this a bug?

Many thanks!

[brianhuey]

@WillAyd Just so I’m clear, you’re suggesting something like: test.groupby('groups').transform({'value1': [np.mean, max], 'value2': max}) which should return something like:

                    value1     value2    
                      mean max   max
2012-02-01 14:00:00      2   3    30
2012-02-01 14:01:00      2   3    30
2012-03-05 14:04:00      2   3    30
2012-03-05 14:01:00      5   6    60
2012-03-10 14:02:00      5   6    60
2012-03-11 14:07:50      5   6    60

[zeromh]

Is there any reason the documentation says that transform takes a dictionary, when it doesn’t?