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Pandas dataframe.value_counts（） . keys（）与浮点索引

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我正在尝试使用pandas来获取value_counts . 发出命令时：

my_variable.value_counts().keys()

我得到以下输出：

指数（[1.0,0.0,1.00999999046,2.0,2.00999999046,3.0,1.01002222888,3.00999999046,4.00999999046,4.0,6.00999999046,5.00999999046,8.00999999046,20010022888,5.0,0.0000000009537,9.00999999046,6.0,7.0,12.0099999905,7.00999999046,10.0099999905,300100228888， 19.0199999809,11.0099999905,20.0199999809,8.0,14.0199999809,4.011000022888,5.001000022888,38.0399999619,46.0499999523,40.0399999619,20.0299999714,16.0199999809,18.0299999714,9.01999998093,11.0199999809,21.0199999809，-10651.4099998，-4643.13999987，-6388.92000008，-5779.98000002]，dtype = object）

问题是，如何访问由浮点值组成的键，例如键1.00999999046？

我可以使用以下方法访问索引1.0：

my_variable.value_counts()[1]

但是，如果我尝试

my_variable.value_counts()[1.00999999046]

然后我收到一个错误说明：

KeyError：1.00999999046

我认为它可能与dytpe =索引的对象有关，但我不知道该怎么做才能解释这一点 . 任何指导将不胜感激 .

python pandas

1 回答

  • 2

    这在> = 0.13时工作得很好 . 在0.13浮点数之前，指数并不特别 . 他们现在有逻辑来避免将索引器舍入/截断为整数 . 在其他工作中，值被查找，而不是被强制（对于Float64Index） . 事实上，这是这种类型索引的重点，使用 [],ix,loc 制作统一的索引模型，返回相同的精确结果 .

    见the docs

    In [8]: i = Index([1.0, 0.0, 1.00999999046, 2.0, 2.00999999046, 3.0, 1.01000022888, 3.00999999046, 4.00999999046, 4.0, 6.00999999046, 5.00999999046, 8.00999999046, 2.01000022888, 5.0, 0.990000009537, 9.00999999046, 6.0, 7.0, 12.0099999905, 7.00999999046, 10.0099999905, 3.01000022888, 19.0199999809, 11.0099999905, 20.0199999809, 8.0, 14.0199999809, 4.01000022888, 5.01000022888, 38.0399999619, 46.0499999523, 40.0399999619, 20.0299999714, 16.0199999809, 18.0299999714, 9.01999998093, 11.0199999809, 21.0199999809, -10651.4099998, -4643.13999987, -6388.92000008, -5779.98000002])

In [9]: i
Out[9]: Float64Index([1.0, 0.0, 1.00999999046, 2.0, 2.00999999046, 3.0, 1.01000022888, 3.00999999046, 4.00999999046, 4.0, 6.00999999046, 5.00999999046, 8.00999999046, 2.01000022888, 5.0, 0.990000009537, 9.00999999046, 6.0, 7.0, 12.0099999905, 7.00999999046, 10.0099999905, 3.01000022888, 19.0199999809, 11.0099999905, 20.0199999809, 8.0, 14.0199999809, 4.01000022888, 5.01000022888, 38.0399999619, 46.0499999523, 40.0399999619, 20.0299999714, 16.0199999809, 18.0299999714, 9.01999998093, 11.0199999809, 21.0199999809, -10651.4099998, -4643.13999987, -6388.92000008, -5779.98000002], dtype='object')

In [10]: s = Series(i.tolist() * 3)


In [13]: s.value_counts()[1.00999999046]
Out[13]: 3

    请注意，索引的显示是值的截断视图（它们完全存在，这里不打印超出2个位置）

    In [14]: s.value_counts().sort_index()
Out[14]: 
-10651.41    3
-6388.92     3
-5779.98     3
-4643.14     3
 0.00        3
 0.99        3
 1.00        3
 1.01        3
 1.01        3
 2.00        3
 2.01        3
 2.01        3
 3.00        3
 3.01        3
 3.01        3
 4.00        3
 4.01        3
 4.01        3
 5.00        3
 5.01        3
 5.01        3
 6.00        3
 6.01        3
 7.00        3
 7.01        3
 8.00        3
 8.01        3
 9.01        3
 9.02        3
 10.01       3
 11.01       3
 11.02       3
 12.01       3
 14.02       3
 16.02       3
 18.03       3
 19.02       3
 20.02       3
 20.03       3
 21.02       3
 38.04       3
 40.04       3
 46.05       3
dtype: int64

In [15]: s.value_counts()[1.00999999046]
Out[15]: 3

In [16]: s.value_counts().keys()
Out[16]: Float64Index([3.00999999046, 14.0199999809, 2.00999999046, -10651.4099998, 2.01000022888, 18.0299999714, 20.0299999714, 16.0199999809, 6.00999999046, 3.01000022888, 8.0, 11.0199999809, 19.0199999809, 7.0, 1.01000022888, 0.990000009537, 4.0, 3.0, 2.0, 1.0, 46.0499999523, 11.0099999905, 12.0099999905, 4.00999999046, 40.0399999619, 7.00999999046, 9.01999998093, 6.0, -6388.92000008, 21.0199999809, 38.0399999619, 5.0, 20.0199999809, 4.01000022888, -5779.98000002, 1.00999999046, 9.00999999046, -4643.13999987, 5.01000022888, 10.0099999905, 8.00999999046, 5.00999999046, 0.0], dtype='object')
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