仅一列的Z分数归一化不会替换熊猫中的列

Question

我有一个数据集，其中收入是许多变量之一。我想立即在收入变量右边添加一列，即z得分。我知道这里有一个问题，关于如何对除一列或多列之外的所有列执行此操作，但是我需要对一列执行此操作，而不用替换值。这可能是很长的路要走，但是我只提取了收入列，然后将z得分应用于它。但是，我无法弄清楚如何重命名“ Norm_Income”列，然后将其放回到收入旁边的主数据框中。任何帮助是极大的赞赏。这是我所拥有的（我知道不多）：

## HW Part 3:  Standardizing Income Attribute with Z-Score Normalization
Income=pd.DataFrame(bank_df,columns=['income'])
from scipy.stats import zscore
Norm_Income=Income.apply(zscore)
Norm_Income 

编辑：这太奇怪了：昨晚进行这项工作，但是现在我得到一个错误。这是我的代码：

## HW Part 3: Standardizing Income Attribute with Z-Score Normalization Income=pd.DataFrame(bank_df,columns=['income'])
from scipy.stats import zscore
Income["Norm_Income"] = Income.apply(zscore) bank_df=bank_df[["id","age","income","Norm_Income","children","gender","region","married","car","savings_acct","current_acct","mortgage","pep"]]
bank_df 

这是新错误：

Answer 1

您已经有一个系列，因此将其放入数据框中非常简单，请看一下Adding new column to existing DataFrame in Python pandas

您只需要：

Income["Norm_Income"] = Income.apply(zscore)

代替您的第三行

Answer 2

因此，请忽略我对答案的评论。我想通了代码，在我的问题的情况下工作。

## HW Part 3:  Standardizing Income Attribute with Z-Score Normalization
Income=pd.DataFrame(bank_df,columns=['income'])
from scipy.stats import zscore
bank_df["norm_income"] = Income.apply(zscore)
bank_df["norm_income"]
bank_df=bank_df[["id","age","income","norm_income","children","gender","region","married","car","savings_acct","current_acct","mortgage","pep"]]
bank_df