Python 运行函数时Numpy float对象不可编辑

我试图在一些比较条件的基础上为集群分配名称，但是我得到一个错误，声明为numpy float object not iterable。此外，我不希望将数据集子集，正如您在下面看到的，从df到df1，然后再对其进行处理。 代码如下：

import pandas as pd
df = pd.DataFrame({'cluster':[0, 1, 2, 9999], 'earlypc':[88.943,4.034,6.839,0.488],'C':[3.491,8.306,75.329,34.5],'D':[14.548,87.66,17.832,65.012]})
df1=df[df['cluster']!=999]
def cluster(a,b,c,d):
    if(max(b)==b):
        return 'high'
    elif (max(c)==c):
         return 'low'
    elif (max(d)==d):
        return 'medium'
    else: return 'medium'

df1['Vendor_Segmentation']=df1.apply(lambda x:cluster(x['cluster'],x['earlypc'],x['C'],x['D']),axis=1)

TypeError: 'numpy.float64' object is not iterable

---

完全不使用apply如何：

df1['Vendor_Segmentation'] = 'medium'
df1.loc[df1.earlypc==df1.earlypc.max(), "Vendor_Segmentation"] = 'high'
df1.loc[df1.C==df1.C.max(), "Vendor_Segmentation"] = 'low'
df1.loc[df1.D==df1.D.max(), "Vendor_Segmentation"] = 'medium'

这就得到了期望的结果

    cluster earlypc C   D        Vendor_Segmentation
0   0       88.943  3.491   14.548      high
1   1       4.034   8.306   87.660      medium
2   2       6.839   75.329  17.832      low
3   9999    0.488   34.500  65.012      medium

---

我支持上述观点，不使用

apply

，并从

numpy

软件包中提供了两种备选方案，它们是为您这样的情况而设计的：

import numpy as np
import pandas as pd

二,

打印：

Out[531]: 

   cluster  earlypc       C       D Vendor_segmentation
0        0   88.943   3.491  14.548                high
1        1    4.034   8.306  87.660              medium
2        2    6.839  75.329  17.832                 low
3     9999    0.488  34.500  65.012              medium

或

=

是等效的

df1['Vendor_segmentation'] = np.where(df1['earlypc'].eq(df1['earlypc'].max()),'high',
                              np.where(df1['C'].eq(df1['C'].max()),'low',
                              np.where(df1['D'].eq(df1['D'].max()),'medium',
                                                                   'medium')))

Out[531]: 

   cluster  earlypc       C       D Vendor_segmentation
0        0   88.943   3.491  14.548                high
1        1    4.034   8.306  87.660              medium
2        2    6.839  75.329  17.832                 low
3     9999    0.488  34.500  65.012              medium