python - Normalise between 0 and 1 ignoring NaN

Question

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).

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.

Answer 1

consider pd.Series s

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

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Option 1
Min Max Scaling

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

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

Option 2
sigmoid

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

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

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Option 3
tanh (hyperbolic tangent)

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