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python - 如何在 python 中使用循环有效地进行特征工程？

问题描述

我正在尝试执行以下操作：

df['SR1'] = df['Open'].pct_change(1)
df['SR2'] = df['Open'].pct_change(2)
df['SR3'] = df['Open'].pct_change(3)
df['SR4'] = df['Open'].pct_change(4)
df['SR5'] = df['Open'].pct_change(5)

df['SR6'] = df['Open'].pct_change(6)
df['SR7'] = df['Open'].pct_change(7)
df['SR8'] = df['Open'].pct_change(8)
df['SR9'] = df['Open'].pct_change(9)
df['SR10'] = df['Open'].pct_change(10)

df['SR11'] = df['Open'].pct_change(11)
df['SR12'] = df['Open'].pct_change(12)
df['SR13'] = df['Open'].pct_change(13)
df['SR14'] = df['Open'].pct_change(14)
df['SR15'] = df['Open'].pct_change(15)

df['SR16'] = df['Open'].pct_change(16)
df['SR17'] = df['Open'].pct_change(17)
df['SR18'] = df['Open'].pct_change(18)
df['SR19'] = df['Open'].pct_change(19)
df['SR20'] = df['Open'].pct_change(20)

df['SR30'] = df['Open'].pct_change(30)
df['SR50'] = df['Open'].pct_change(50)
df['SR70'] = df['Open'].pct_change(70)
df['SR90'] = df['Open'].pct_change(90)

df['SR110'] = df['Open'].pct_change(110)
df['SR130'] = df['Open'].pct_change(130)
df['SR150'] = df['Open'].pct_change(150)
df['SR170'] = df['Open'].pct_change(170)
df['SR190'] = df['Open'].pct_change(190)

df['SR210'] = df['Open'].pct_change(210)
df['SR230'] = df['Open'].pct_change(230)
df['SR250'] = df['Open'].pct_change(250)

它看起来愚蠢而低效。有没有很酷的方法来创建一个循环这个函数？我只是无法将数字放在 pct_change() 的括号内。

标签： pythonpandasloops

解决方案

为什么不是一个简单的for循环？

for n in list(range(1, 20)) + list(range(30, 270, 20)):
    df[f'SR{n}'] = df['Open'].pct_change(n)

注意：f 字符串表示法仅适用于 Python >= 3.6，并且等效于'SR{}'.format(n).

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