multipliers = {'A' : 5, 'B' : 10, 'C' : 15, 'D' : 20}
df = pd.util.testing.makeDataFrame() # a random df with columns A,B,C,D
f = lambda x, col: multipliers[col] * x
是否有一种非循环的方式将f应用于每列，例如df.apply(f, axis = 0, ?)？我能用loop实现的是
df2 = df.copy()
for c in df.columns:
df2[c] = f(df[c], c)
(实数f比上述示例更复杂，请将f视为两个变量的黑盒函数，arg1是数据，arg2是列名)
使用lambda函数，对于传递列名，使用x.name:
np.random.seed(2022)
multipliers = {'A' : 5, 'B' : 10, 'C' : 15, 'D' : 20}
df = pd.util.testing.makeDataFrame() # a random df with columns A,B,C,D
f = lambda x, col: multipliers[col] * x
df2 = df.copy()
for c in df.columns:
df2[c] = f(df[c], c)
print (df2.head())
A B C D
9CTWXXW3ys 2.308860 6.375789 5.362095 -23.354181
yq1PHBltEO 2.876024 1.950080 15.772909 -13.776645
lWtMioDq6A -11.206739 17.691500 -12.175996 25.957264
lEHcq1pxLr -6.510434 -6.004475 14.084401 13.999673
xvL04Y66tm -3.827731 -3.104207 -4.111277 1.440596
df2 = df.apply(lambda x: f(x, x.name))
print (df2.head())
A B C D
9CTWXXW3ys 2.308860 6.375789 5.362095 -23.354181
yq1PHBltEO 2.876024 1.950080 15.772909 -13.776645
lWtMioDq6A -11.206739 17.691500 -12.175996 25.957264
lEHcq1pxLr -6.510434 -6.004475 14.084401 13.999673
xvL04Y66tm -3.827731 -3.104207 -4.111277 1.440596