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Pandas dataframe.value_counts//.keys// 随着浮动指标

我正在努力使用 pandas 为了获得 value_counts. 运行命令时：


my_variable.value_counts//.keys//


我得出以下结论：

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, 9.01999998093, 11.0199999809, 21.0199999809, -10651.4099998, -4643.13999987, -6388.92000008, -5779.98000002], dtype =对象/

问题是如何访问由浮点值组成的密钥，例如键 1.00999999046?

我可以访问索引 1.0 通过：


my_variable.value_counts//[1]


但如果我试试


my_variable.value_counts//[1.00999999046]


然后我收到一条错误消息：

KeyError: 1.00999999046

我相信这可以是与索引=索引的对象的事实有关，但我不知道该怎么做才能解释它。 任何指南都将得到理解。
2021-03-20 添加评论
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没有找到相关结果

    已邀请:

    窦买办

    赞同来自:

    它很棒 >= 0.13. 到 0.13 浮动指标不特别。 现在他们有逻辑来避免舍入 / 索引者的中断到整数数字。 in-other-works 价值观正在寻找，不强行 /为了 Float64Index/. 事实上，这种类型的索引的含义是创建一个统一的索引模型
    [],ix,loc

    , 返回相同的准确结果。

    厘米。
    http://pandas.pydata.org/panda ... index

    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，索引的显示具有值的截断视图。 /它们充分存在，只是在这里不超过2个地方打印它们/


    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'/
    2021-03-21 0 0
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