python – pandas SettingWithCopyWarning

DataFrame 의 자료 구조에서  새로운 column을 만들고 column에 값을 넣을 때 아래와 같은 SettingWithCopyWarning 메시지가 발생한다.

<ipython-input-64-04010302304>:1: SettingWithCopyWarning:
A values is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documention:

document 문서를 보면 pandas object에 데이터를 넣을 때 chained indexing 를 주의 하라는 메시지가 나오고 example 코드가 나온다.

In [354]: dfmi = pd.DataFrame([list('abcd'),
   .....:                      list('efgh'),
   .....:                      list('ijkl'),
   .....:                      list('mnop')],
   .....:                     columns=pd.MultiIndex.from_product([['one', 'two'],
   .....:                                                         ['first', 'second']]))

In [355]: dfmi
    one          two       
  first second first second
0     a      b     c      d
1     e      f     g      h
2     i      j     k      l
3     m      n     o      p
In [356]: dfmi['one']['second']
0    b
1    f
2    j
3    n
Name: second, dtype: object
In [357]: dfmi.loc[:, ('one', 'second')]
0    b
1    f
2    j
3    n
Name: (one, second), dtype: object

‘one’,’second’의 column의 값을 확인 하기 위한 방법은 첫번째 방법 dfmi[‘one’][‘second’], 두번째 방법 dfmi.loc[:, (‘one’,’second’) 이 있고 같은 결과를 보여준다.

그럼 두 개의 차이점은??


우선 dfmi[‘one’]를 선택된 개별적인 index를 가진 dataframe를 리턴을 한다.  그리고 다른 python operation 인 dfmi_with_one[‘second’]은  ‘second’로 인덱싱 된 series를 선택하게 된다. 즉 pandas 는 분리된 이벤트로 __getitem__을 호출하여 데이터를 가져오게 되는데, ‘one’ 의 데이터를 가져온 후, ‘second’을 가져오는 방식으로 선형동작에 문제를 일으킬 수 있다.


한번의 __getitem__ 을 호출하여 tuple 집합을 가져온다. pandas는 이 방식을 하나의 객체로 다룬다. 그리고 이러한 연산순서는 속도가 훨씬 빨라 질 수 있고, 두 축을 인덱싱 할 수 있다.

실제로 경고 문구에서 추천하는 방식과 chained indexing 을 통한 동작 방식을 보면 아래와 같다.

dfmi.loc[:, ('one', 'second')] = value
# becomes
dfmi.loc.__setitem__((slice(None), ('one', 'second')), value)
dfmi['one']['second'] = value
# becomes
dfmi.__getitem__('one').__setitem__('second', value)

하지만 chained indexing 을 사용하지 않는 경우에도 경고 문구가 발생할 수 있다. 하지만 SettingWithCopyWarning이 해당 코드의 버그를 주지 시키기 위한 의도된 동작인걸로 보여진다.

그리고 해당 경고에 대한 pandas 옵션을 제공해 주고 있다

pd.set_option('mode.chained_assignment','warn') : default 옵션
pd.set_option('mode.chained_assignment','raise') : SettingWithCopyException 오류를 발생시킨다.
pd.set_option('mode.chained_assignment', None) : 해당 경고 자체를 발생하지 않는다.

그리고 SettingWithCopyWarning 의 문제를 해결하기 위하여 추전하는 방법으로는 아래와 같이 .copy() 를 사용하는 것이다.

In [360]: dfc = pd.DataFrame({'a': ['one', 'one', 'two',
   .....:                           'three', 'two', 'one', 'six'],
   .....:                     'c': np.arange(7)})

In [361]: dfd = dfc.copy()

# Setting multiple items using a mask
In [362]: mask = dfd['a'].str.startswith('o')

In [363]: dfd.loc[mask, 'c'] = 42

In [364]: dfd
       a   c
0    one  42
1    one  42
2    two   2
3  three   3
4    two   4
5    one  42
6    six   6

# Setting a single item
In [365]: dfd = dfc.copy()

In [366]: dfd.loc[2, 'a'] = 11

In [367]: dfd
       a  c
0    one  0
1    one  1
2     11  2
3  three  3
4    two  4
5    one  5
6    six  6



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