Question

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， 1.01000022888，3.00999999046，4.00999999046，4.0，6.00999999046，5.00999999046，8.00999999046，2.01000022888，5.0 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， 46.0499999523,40.0399999619,20.0299999714,16.0199999809,18.0299999714,9.0119999998093,11.0199999809,21.0199999809，-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：

访问索引1.0

my_variable.value_counts()[1]

但是，如果我尝试

my_variable.value_counts()[1.00999999046]

然后我收到错误声明：

KeyError：1.00999999046

我认为这可能与指数的dytpe =对象有关，但我不知道该怎么做才能解释这一点。任何指导都将不胜感激。

Answer 1

这在＆gt; = 0.13中工作得很好。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[],ix,loc的统一索引模型返回相同的结果。

请参阅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，只是不打印超出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')

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

1 个答案: