如何使用 pd.cut 对 nan 值进行分箱

Question

我正在尝试编写一个代码，从包含空白值的数据帧（account_raw）创建垃圾箱。我的问题是 python 用我的第一个 bin 标签存储空白值：0 - 25k。我想要做的是为空白值创建一个单独的容器。有什么想法如何解决这个问题吗？谢谢

Bucket = [0, 25000, 50000, 100000,
          200000, 300000, 999999999999]
Label = ['0k to 25k', '25k - 50k',
         '50k - 100k', '100k - 200k',
         '200k - 300k', 'More than 300k']

account_raw['LoanGBVBuckets'] = pd.cut(
    account_raw['IfrsBalanceEUR'],
    bins=ls_LoanGBVBucket,
    labels=ls_LoanGBVBucketLabel,
    include_lowest=True).astype(str)

Answer 1

我认为最简单的是处理值并按列pd.cut设置缺失值的自定义类别IfrsBalanceEUR：

account_raw['LoanGBVBuckets'] = pd.cut(account_raw['IfrsBalanceEUR'],
                                      bins=ls_LoanGBVBucket, 
                                      labels=ls_LoanGBVBucketLabel, 
                                      include_lowest= True).astype(str)

account_raw.loc[account_raw['IfrsBalanceEUR'].isna(), 'LoanGBVBuckets'] = 'missing values'

编辑：

在 pandas 0.25.0 中进行了测试，对于缺失值，NaN在输出中获取 s ，为了替换它们，首先需要某个类别cat.add_categories，然后fillna：

account_raw = pd.DataFrame({'IfrsBalanceEUR':[np.nan, 100, 100000]})

Bucket = [0, 25000, 50000, 100000, 200000, 300000, 999999999999]
Label = ['0k to 25k', '25k - 50k', '50k - 100k', 
         '100k - 200k', '200k - 300k', 'More than 300k']

account_raw['LoanGBVBuckets'] = pd.cut(account_raw['IfrsBalanceEUR'],
                                      bins=Bucket, 
                                      labels=Label, 
                                      include_lowest= True)
print (account_raw)
   IfrsBalanceEUR LoanGBVBuckets
0             NaN            NaN
1           100.0      0k to 25k
2        100000.0     50k - 100k

account_raw['LoanGBVBuckets']=(account_raw['LoanGBVBuckets'].cat
                                                            .add_categories('missing values')
                                                            .fillna('missing values'))
print (account_raw)
   IfrsBalanceEUR  LoanGBVBuckets
0             NaN  missing values
1           100.0       0k to 25k
2        100000.0      50k - 100k