在Pandas中將MultiIndex的級別重新索引為任意順序

Question

我有一些代碼總結了一個包含着名的泰坦尼克號數據集的DataFrame，如下所示：

titanic['agecat'] = pd.cut(titanic.age, [0, 13, 20, 64, 100], 
               labels=['child', 'adolescent', 'adult', 'senior'])
titanic.groupby(['agecat', 'pclass','sex']
                )['survived'].mean()

這將生成以下DataFrame，其中包含基於groupby調用的MultiIndex：

agecat      pclass  sex   
adolescent  1       female    1.000000
                    male      0.200000
            2       female    0.923077
                    male      0.117647
            3       female    0.542857
                    male      0.125000
adult       1       female    0.965517
                    male      0.343284
            2       female    0.868421
                    male      0.078125
            3       female    0.441860
                    male      0.159184
child       1       female    0.000000
                    male      1.000000
            2       female    1.000000
                    male      1.000000
            3       female    0.483871
                    male      0.324324
senior      1       female    1.000000
                    male      0.142857
            2       male      0.000000
            3       male      0.000000
Name: survived, dtype: float64

但是，我希望agecat級別是自然排序的，而不是按字母排序，即： ['child', 'adolescent', 'adult', 'senior'] 。 但是，如果我嘗試使用reindex來執行此操作：

titanic.groupby(['agecat', 'pclass','sex'])['survived'].mean().reindex(
    ['child', 'adolescent', 'adult', 'senior'], level='agecat')

它對生成的DataFrame的MultiIndex沒有任何影響。 這應該有效，還是我使用了錯誤的方法？

Answer 1

您需要提供重新排序的MultiIndex

In [36]: index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
                                   ['one', 'two', 'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])

In [37]: df = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

In [38]: df
Out[38]: 
exp                  A         B         C
first second                              
foo   one    -1.007742  2.594146  1.211697
      two     1.280218  0.799940  0.039380
      three  -0.501615 -0.136437  0.997753
bar   one    -0.201222  0.060552  0.480552
      two    -0.758227  0.457597 -0.648014
baz   two    -0.326620  1.046366 -2.047380
      three   0.395894  1.128850 -1.126649
qux   one    -0.353886 -1.200079  0.493888
      two    -0.124532  0.114733  1.991793
      three  -1.042094  1.079344 -0.153037

通過在第二級進行排序來模擬重新排序

In [39]: idx = df.sortlevel(level='second').index

In [40]: idx
Out[40]: 
MultiIndex
[(u'foo', u'one'), (u'bar', u'one'), (u'qux', u'one'), (u'foo', u'two'), (u'bar', u'two'), (u'baz', u'two'), (u'qux', u'two'), (u'foo', u'three'), (u'baz', u'three'), (u'qux', u'three')]

In [41]: df.reindex(idx)
Out[41]: 
exp                  A         B         C
first second                              
foo   one    -1.007742  2.594146  1.211697
bar   one    -0.201222  0.060552  0.480552
qux   one    -0.353886 -1.200079  0.493888
foo   two     1.280218  0.799940  0.039380
bar   two    -0.758227  0.457597 -0.648014
baz   two    -0.326620  1.046366 -2.047380
qux   two    -0.124532  0.114733  1.991793
foo   three  -0.501615 -0.136437  0.997753
baz   three   0.395894  1.128850 -1.126649
qux   three  -1.042094  1.079344 -0.153037

不同的訂購

In [42]: idx = idx[5:] + idx[:5]

In [43]: idx
Out[43]: 
MultiIndex
[(u'bar', u'one'), (u'bar', u'two'), (u'baz', u'three'), (u'baz', u'two'), (u'foo', u'one'), (u'foo', u'three'), (u'foo', u'two'), (u'qux', u'one'), (u'qux', u'three'), (u'qux', u'two')]

In [44]: df.reindex(idx)
Out[44]: 
exp                  A         B         C
first second                              
bar   one    -0.201222  0.060552  0.480552
      two    -0.758227  0.457597 -0.648014
baz   three   0.395894  1.128850 -1.126649
      two    -0.326620  1.046366 -2.047380
foo   one    -1.007742  2.594146  1.211697
      three  -0.501615 -0.136437  0.997753
      two     1.280218  0.799940  0.039380
qux   one    -0.353886 -1.200079  0.493888
      three  -1.042094  1.079344 -0.153037
      two    -0.124532  0.114733  1.991793