Python pandas.DataFrame在用ix, loc, iloc 索引的时候就相当于拷贝copy了一个对象

pandas 索引问题

有时候我们会经常把一个DataFrame传进一个函数里，又害怕它被函数里面的语句更改了，所以我一般做法是先df.copy()一下，但是其实我们在用ix, iloc, loc 索引的时候就已经进行了拷贝，下面举个例子

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In [52]: df = pd.DataFrame(np.arange(10).reshape(2,5), columns=list('abcde'), index=list('mn'))

In [53]: df
Out[53]: 
   a  b  c  d  e
m  0  1  2  3  4
n  5  6  7  8  9

In [54]: aa = df.loc[:, ['a','b']]

In [55]: aa
Out[55]: 
   a  b
m  0  1
n  5  6
In [56]: aa['a'][0] = 3

In [57]: df
Out[57]: 
   a  b  c  d  e
m  0  1  2  3  4
n  5  6  7  8  9

In [58]: aa
Out[58]: 
   a  b
m  3  1
n  5  6

In [59]: aa = df.iloc[:, [0,1]]

In [60]: aa
Out[60]: 
   a  b
m  0  1
n  5  6

In [61]: aa['a'][0] = 3
C:\Apps\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:2910: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  exec(code_obj, self.user_global_ns, self.user_ns)

In [66]: aa
Out[66]: 
m    0
n    5
Name: a, dtype: int32

In [67]: aa[0] = 3

In [68]: df
Out[68]: 
   a  b  c  d  e
m  3  1  2  3  4
n  5  6  7  8  9

In [69]: aa = df.loc[:, 'b']

In [70]: aa[0] = 3

In [71]: df
Out[71]: 
   a  b  c  d  e
m  3  3  2  3  4
n  5  6  7  8  9

可以发现当选择的列数 大于1 的时候是copy了的，但是如果只选择一列，则没copy，在对其赋值的时候要格外小心。最好还是copy一下吧