MultiIndex / Advanced Indexing,其中一个级别不是(!=)一个值

时间:2016-07-24 23:03:48

标签: python pandas indexing slice

如何切割以下df,使第二级!= 2。

在我的真实案例中,我的第二个级别是日期范围,我希望能够选择除一个日期之外的所有内容。

来自MultiIndex / Advanced Indexing

In [1]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                 ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
In [2]: tuples = list(zip(*arrays))
In [4]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
In [16]: df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
In [38]: df = df.T

In [65]: df
Out[65]: 
                     A         B         C
first second                              
bar   one     0.895717  0.410835 -1.413681
      two     0.805244  0.813850  1.607920
baz   one    -1.206412  0.132003  1.024180
      two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
      two     1.340309 -1.187678 -2.211372
qux   one    -1.170299  1.130127  0.974466
      two    -0.226169 -1.436737 -2.006747

In [66]: df.xs('one', level='second')
Out[66]: 
              A         B         C
first                              
bar    0.895717  0.410835 -1.413681
baz   -1.206412  0.132003  1.024180
foo    1.431256 -0.076467  0.875906
qux   -1.170299  1.130127  0.974466

我很惊讶文档@ pandas.pydata.org太差了。对于任何示例都没有解释。就像文档是由专家为那些已经熟悉熊猫所有功能的人们编写的。

为什么文档没有提供重新生成示例的代码?

1 个答案:

答案 0 :(得分:2)

从这开始:

                    A         B         C
first second                              
bar   one    -0.350640 -1.761671  0.253923
      two    -0.036557  0.212322  0.537106
baz   one    -1.597584 -0.301356 -0.634428
      two     2.340900 -0.356272 -0.985386
foo   one     0.122753 -0.333827 -0.620175
      two     0.423211 -0.570563 -1.245026
qux   one    -0.972814 -0.878836 -1.030892
      two     0.312855 -0.191677  0.700006


df.iloc[df.index.get_level_values('second') != 'one' ]

                    A         B         C
first second                              
bar   two    -0.036557  0.212322  0.537106
baz   two     2.340900 -0.356272 -0.985386
foo   two     0.423211 -0.570563 -1.245026
qux   two     0.312855 -0.191677  0.700006


df.iloc[df.index.get_level_values('second') != 'two' ]
                     A         B         C
first second                              
bar   one    -0.350640 -1.761671  0.253923
baz   one    -1.597584 -0.301356 -0.634428
foo   one     0.122753 -0.333827 -0.620175
qux   one    -0.972814 -0.878836 -1.030892