python - 在 Pandas 数据框列中重新编码除一个值外的所有值

Question

我正在尝试重新编码有关贷款状态的数据，以便每次观察都是违约或全额支付。具体来说，我想将 anyone != 'Fully Paid' 重新编码为 'Default'。

这是我的值(value)观:

df.loan_status.unique()

array(['Fully Paid', 'Charged Off', 'Default', 'Late (31-120 days)',
   'In Grace Period', 'Late (16-30 days)',
   'Does not meet the credit policy. Status:Fully Paid',
   'Does not meet the credit policy. Status:Charged Off', 'Issued'], dtype=object)

我尝试了以下代码，但所有观察结果都被重新编码为“默认”:

statuses= df['loan_status'].unique()
for status in statuses:
    if status!='Fully Paid':
        df['loan_status']='Default'

如有任何关于如何执行此操作的建议，我们将不胜感激!

Answer 1

我喜欢这种方法。

Andras Deak/MaxU；选项 1

df.loc[df.loan_status.ne('Fully Paid'), 'loan_status'] = 'Default'

选项 2
pd.Series.where

ls = df.loan_status
df.update(ls.where(ls.eq('Fully Paid'), 'Default'))

选项 3
pd.Series.mask

ls = df.loan_status
df.update(ls.mask(ls.ne('Fully Paid')).fillna('Default'))

选项 4
numpy.where

ls = df.loan_status.values
paid, dflt = 'Fully Paid', 'Default'
df.loc[:, 'loan_status'] = np.where(ls == paid, paid, dflt)