python確實很用來很爽很蘇服，代碼不多
各種庫出於易用目的，做了很多默認設置，但要是不認真看API，那就會掉到坑里了。
df1.groupby(['Dn','UserLabel','BeginTime']).first()
df1['city']=df1['UserLabel'].str.slice(0,2)
出現
df1['UserLabel']
File "D:\script\Python279\lib\site-packages\pandas\core\frame.py", line 1787, in __getitem__
return self._getitem_column(key)
File "D:\script\Python279\lib\site-packages\pandas\core\frame.py", line 1794, in _getitem_column
return self._get_item_cache(key)
File "D:\script\Python279\lib\site-packages\pandas\core\generic.py", line 1079, in _get_item_cache
values = self._data.get(item)
File "D:\script\Python279\lib\site-packages\pandas\core\internals.py", line 2843, in get
loc = self.items.get_loc(item)
File "D:\script\Python279\lib\site-packages\pandas\core\index.py", line 1437, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas\index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas\index.c:3824)
File "pandas\index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas\index.c:3704)
File "pandas\hashtable.pyx", line 697, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12349)
File "pandas\hashtable.pyx", line 705, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12300)
KeyError: 'UserLabel'
因為中間過程將df1.to_pickle成文件，一直以為是pickle問題，以為是Userlabel是Unicode導致的問題，最后細看pandas的api文檔才發現這一切是因groupby()的默認參數所致。
An obvious one is aggregation via the aggregate or equivalently agg method:
In [40]: grouped = df.groupby('A') In [41]: grouped.aggregate(np.sum) Out[41]: C D A bar 0.443469 0.920834 foo 2.529056 -1.724719 In [42]: grouped = df.groupby(['A', 'B']) In [43]: grouped.aggregate(np.sum) Out[43]: C D A B bar one -0.042379 -0.089329 three -0.009920 -0.945867 two 0.495767 1.956030 foo one -0.556905 -1.113758 three 1.548106 -0.016692 two 1.537855 -0.594269
As you can see, the result of the aggregation will have the group names as the new index along the grouped axis. In the case of multiple keys, the result is a MultiIndex by default, though this can be changed by using the as_index option:
In [44]: grouped = df.groupby(['A', 'B'], as_index=False) In [45]: grouped.aggregate(np.sum) Out[45]: A B C D 0 bar one -0.042379 -0.089329 1 bar three -0.009920 -0.945867 2 bar two 0.495767 1.956030 3 foo one -0.556905 -1.113758 4 foo three 1.548106 -0.016692 5 foo two 1.537855 -0.594269 In [46]: df.groupby('A', as_index=False).sum() Out[46]: A C D 0 bar 0.443469 0.920834 1 foo 2.529056 -1.724719
Note that you could use the reset_index DataFrame function to achieve the same result as the column names are stored in the resulting MultiIndex:
as_index默認為true，由於groupby后，'Dn','UserLabel','BeginTime'都由column變成了index，多個index(MultiIndex)，index無法用df1[列名]來表示
所以需要在groupby時加上as_index=False參數，或用reindex()