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 val

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()