根据其他列的值创建新列

Question

在我的df中，我为下面的每个实体(Grubhub、Toasttab、Tenk)都有一个列，它对每一行的值表示是或否，

我的代码如下：

df['Grubhub'] = df[['On GrubHub or Seamless?']].apply(lambda x: any(x == 'Yes'), axis = 1)

df['ToastTab'] = df[['On ToastTab?']].apply(lambda x: any(x == 'Yes'), axis = 1)

df['Tenk'] = df[['On Tenk?']].apply(lambda x: any(x == 'Yes'), axis = 1)

df['Udemy'] = df[['On Udmey?']].apply(lambda x: any(x == 'Yes'), axis = 1)

df['Postmates'] = df[['On Postmates?']].apply(lambda x: any(x == 'Yes'), axis = 1)

df['Doordash'] = df[['On DoorDash?']].apply(lambda x: any(x == 'Yes'), axis = 1)

df['Google'] = df[['On Goole?']].apply(lambda x: any(x == 'Yes'), axis = 1)

这为每个实体( Grubhub、Toasttab、Tenk )提供了一个新列，并且该列给出了一个假值，是否有一种更有效的方法，可以在一行代码或函数中完成所有这些操作？谢谢你提前帮忙

Answer 1

您可以创建列映射并在function中应用loop。

columns_map = (
    ('Grubhub', 'On GrubHub or Seamless?'),
    ('ToastTab', 'On ToastTab?'),
    ('Tenk', 'On Tenk?'),
    # etc ...
)

for new_col, alias in columns_map:
    df[new_col] = df[alias].apply(lambda x: x == 'Yes')
    # also you can easily remove aliases columns:
    # df = df.drop(columns=[alias])

或者，可以将值设置为原始列，并在需要时重命名(不使用drop())：

for new_col, alias in columns_map:
    df[alias] = df[alias].apply(lambda x: x == 'Yes')

df.rename(
    columns={alias: new_col for new_col, alias in columns_map},
    inplace=True
)