忽略NaN归一化在0和1之间 [英] Normalise between 0 and 1 ignoring NaN

问题描述

对于从x到y的可能包含NaN的数字​​列表，我如何在0和1之间进行归一化，而忽略NaN值(它们保持为NaN).

For a list of numbers ranging from x to y that may contain NaN, how can I normalise between 0 and 1, ignoring the NaN values (they stay as NaN).

通常，我会使用MinMaxScaler((a >参考页)，但无法处理NaN，建议根据均值或中位数等推算值.它不提供忽略所有NaN值的选项. /p>

Typically I would use MinMaxScaler (ref page) from sklearn.preprocessing, but this cannot handle NaN and recommends imputing the values based on mean or median etc. it doesn't offer the option to ignore all the NaN values.

推荐答案

考虑pd.Series s

s = pd.Series(np.random.choice([3, 4, 5, 6, np.nan], 100))
s.hist()

选项1
最小最大缩放比例

Option 1
Min Max Scaling

new = s.sub(s.min()).div((s.max() - s.min()))
new.hist()

没什么要求
我把它们放进去是因为我想

NOT WHAT OP ASKED FOR
I put these in because I wanted to

选项2
乙状结肠

Option 2
sigmoid

sigmoid = lambda x: 1 / (1 + np.exp(-x))

new = sigmoid(s.sub(s.mean()))
new.hist()

选项3
tanh(双曲正切)

Option 3
tanh (hyperbolic tangent)

new = np.tanh(s.sub(s.mean())).add(1).div(2)
new.hist()

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