Python pandas groupby key error in pandas.hashtable.PyObjectHashTable.get_item

Question

I am doing what seems to be a simple group by in Pandas. The column is a string column with no NaN's or weird strings. However, I keep getting the below error. Does anyone know why this mights happen? I feel like it may have something to do with my data, but it all seems to be ok...

I am running by_user = df.groupby('User')

and the stack trace:

by_user = df.groupby('User')
File "c:\Anaconda\lib\site-packages\pandas\core\generic.py", line 2773, in groupby
sort=sort, group_keys=group_keys, squeeze=squeeze)
File "c:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 1142, in groupby
return klass(obj, by, **kwds)
File "c:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 388, in __init__ level=level, sort=sort)
File "c:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 2041, in _get_grouper
gpr = obj[gpr]
File "c:\Anaconda\lib\site-packages\pandas\core\frame.py", line 1678, in __getitem__
return self._getitem_column(key)
File "c:\Anaconda\lib\site-packages\pandas\core\frame.py", line 1685, in _get      item_column
return self._get_item_cache(key)
File "c:\Anaconda\lib\site-packages\pandas\core\generic.py", line 1052, in _ge
t_item_cache
values = self._data.get(item)
File "c:\Anaconda\lib\site-packages\pandas\core\internals.py", line 2565, in get
loc = self.items.get_loc(item)
File "c:\Anaconda\lib\site-packages\pandas\core\index.py", line 1181, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "index.pyx", line 129, in pandas.index.IndexEngine.get_loc (pandas\index.
c:3656)
File "index.pyx", line 149, in pandas.index.IndexEngine.get_loc (pandas\index.
c:3534)
File "hashtable.pyx", line 696, in pandas.hashtable.PyObjectHashTable.get_item
(pandas\hashtable.c:11911)
File "hashtable.pyx", line 704, in pandas.hashtable.PyObjectHashTable.get_item
(pandas\hashtable.c:11864)
KeyError: 'User'

df.info():

User Code        175167 non-null object
Version          175167 non-null object
Date Accessed    175167 non-null datetime64[ns]
Series           175167 non-null object
Software         175167 non-null object
User             175167 non-null object

Answer 1

[moved from comments]

It's easy to miss trailing whitespace in column names, but you can check df.columns manually:

>>> df = pd.DataFrame({"User": [1,2]})
>>> df2 = pd.DataFrame({"User ": [1,2]})
>>> df
   User
0     1
1     2
>>> df2
   User 
0      1
1      2
>>> df.columns
Index([u'User'], dtype='object')
>>> df2.columns
Index([u'User '], dtype='object')

(To peel back the curtain a bit, I suspected something like this might be going on because when I mocked up my own DataFrame and looked at df.info(), I didn't see as much space between the column names and the numbers as your output seemed to show.)