有没有更好的方法通过将 'na' 替换为均值来将 'object' 类型数组转换为 numpy 数组？ [复制]答案

【问题标题】：Is there a better way to convert 'object' type array to numpy array by replacing 'na' with mean? [duplicate]有没有更好的方法通过将 'na' 替换为均值来将 'object' 类型数组转换为 numpy 数组？ [复制]
【发布时间】：2018-01-09 08:08:57
【问题描述】：

我有一个字符串数组，其中包含一些元素，例如 'na'，这些元素无法通过使用给定的here 中的x.astype(np.float) 转换为浮点数。

请提出比我做的更好的方法。请在下面找到过程（它是我的 jupyter notebook 中的一个 sn-p，我展示了中间步骤只是为了演示更改）：

在[4]中：val_inc

输出[4]：

array(['na', '38.012', '38.7816', '38.0736', '40.7118', '44.7382',
       '39.6416', '38.9177', '36.9031', 43.2611, '38.2732', 40.7129,
       '37.2844', '39.5835', 43.9194, '42.5485', '36.9052', 'na', 41.9264,
       45.3568, '44.6239', 38.1079, 45.2393, '32.785', '44.6239',
       '38.0216', '38.4608', '42.5644', '35.3127', 33.2936, '33.0556',
       '40.4476', 35.6581, '35.5574', '43.1096', '34.4751', 42.0554,
       40.3944, '40.2466', '32.2567', 'na', '38.8594', '43.947', 41.7973,
       '41.8105', 40.3797, 31.2868, '45.3644', '40.7177', '41.8558',
       '38.9249', '33.2077', '42.4053', '42.559'], dtype=object)

在 [5] 中：val_inc[val_inc == 'na']='0'

在[6]中：val_inc

出[6]：

array(['0', '38.012', '38.7816', '38.0736', '40.7118', '44.7382',
       '39.6416', '38.9177', '36.9031', 43.2611, '38.2732', 40.7129,
       '37.2844', '39.5835', 43.9194, '42.5485', '36.9052', '0', 41.9264,
       45.3568, '44.6239', 38.1079, 45.2393, '32.785', '44.6239',
       '38.0216', '38.4608', '42.5644', '35.3127', 33.2936, '33.0556',
       '40.4476', 35.6581, '35.5574', '43.1096', '34.4751', 42.0554,
       40.3944, '40.2466', '32.2567', '0', '38.8594', '43.947', 41.7973,
       '41.8105', 40.3797, 31.2868, '45.3644', '40.7177', '41.8558',
       '38.9249', '33.2077', '42.4053', '42.559'], dtype=object)

在[7]中：val_inc = val_inc.astype(np.float)

在[8]中：val_inc

出[8]：

array([  0.    ,  38.012 ,  38.7816,  38.0736,  40.7118,  44.7382,
        39.6416,  38.9177,  36.9031,  43.2611,  38.2732,  40.7129,
        37.2844,  39.5835,  43.9194,  42.5485,  36.9052,   0.    ,
        41.9264,  45.3568,  44.6239,  38.1079,  45.2393,  32.785 ,
        44.6239,  38.0216,  38.4608,  42.5644,  35.3127,  33.2936,
        33.0556,  40.4476,  35.6581,  35.5574,  43.1096,  34.4751,
        42.0554,  40.3944,  40.2466,  32.2567,   0.    ,  38.8594,
        43.947 ,  41.7973,  41.8105,  40.3797,  31.2868,  45.3644,
        40.7177,  41.8558,  38.9249,  33.2077,  42.4053,  42.559 ])

在[9]中：np.mean(val_inc[val_inc!=0.])

出[9]：39.587374509803915

在[10]中：val_inc[val_inc==0.]=np.mean(val_inc[val_inc!=0.])

在[11]中：val_inc

输出[11]：

array([ 39.58737451,  38.012     ,  38.7816    ,  38.0736    ,
        40.7118    ,  44.7382    ,  39.6416    ,  38.9177    ,
        36.9031    ,  43.2611    ,  38.2732    ,  40.7129    ,
        37.2844    ,  39.5835    ,  43.9194    ,  42.5485    ,
        36.9052    ,  39.58737451,  41.9264    ,  45.3568    ,
        44.6239    ,  38.1079    ,  45.2393    ,  32.785     ,
        44.6239    ,  38.0216    ,  38.4608    ,  42.5644    ,
        35.3127    ,  33.2936    ,  33.0556    ,  40.4476    ,
        35.6581    ,  35.5574    ,  43.1096    ,  34.4751    ,
        42.0554    ,  40.3944    ,  40.2466    ,  32.2567    ,
        39.58737451,  38.8594    ,  43.947     ,  41.7973    ,
        41.8105    ,  40.3797    ,  31.2868    ,  45.3644    ,
        40.7177    ,  41.8558    ,  38.9249    ,  33.2077    ,
        42.4053    ,  42.559     ])

【问题讨论】：

把'na'换成'nan'，就可以转换成浮点数了。
@kazemakase 感谢您的建议。我不知道字符串 'nan' 可以直接转换为 np.nan
很抱歉我的问题被证明是重复的，我会努力提高我的搜索技巧。
无需道歉......相反，您的问题被标记为重复问题现在可以作为其他可能正在寻找与您相同的搜索字词的人的路标。跨度>

标签： python arrays string numpy

【解决方案1】：

将'na' 替换为'nan'，然后将其转换为np.nan，然后使用np.nanmean。

示例：

test = np.array(['0','1','nan'], dtype=float)
np.where(np.isnan(test), np.nanmean(test), test)

array([ 0. ,  1. ,  0.5])

【讨论】：

在其他建议中，您的建议是解决我的问题的最快方法。谢谢！

【解决方案2】：

最好先将“na”转换为正确的 NaN。然后就可以随心所欲地使用数据了：

import numpy as np
val_inc[val_inc == 'na'] = np.nan   # 'na' to proper NaN or missing value
val_inc = val_inc.astype(np.float)  # no error here now.
print(val_inc)

输出：

[     nan  38.012   38.7816  38.0736  40.7118  44.7382  39.6416  38.9177
  36.9031  43.2611  38.2732  40.7129  37.2844  39.5835  43.9194  42.5485
  36.9052      nan  41.9264  45.3568  44.6239  38.1079  45.2393  32.785
  44.6239  38.0216  38.4608  42.5644  35.3127  33.2936  33.0556  40.4476
  35.6581  35.5574  43.1096  34.4751  42.0554  40.3944  40.2466  32.2567
      nan  38.8594  43.947   41.7973  41.8105  40.3797  31.2868  45.3644
  40.7177  41.8558  38.9249  33.2077  42.4053  42.559 ]

【讨论】：