Pythonic way to replace list values with upper and lower bound (clamping, clipping, thresholding)?

You can use numpy.clip:

In [1]: import numpy as npIn [2]: arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])In [3]: lowerBound, upperBound = 3, 7In [4]: np.clip(arr, lowerBound, upperBound, out=arr)Out[4]: array([3, 3, 3, 3, 4, 5, 6, 7, 7, 7])In [5]: arrOut[5]: array([3, 3, 3, 3, 4, 5, 6, 7, 7, 7])

For an alternative that doesn't rely on numpy, you could always do

arr = [max(lower_bound, min(x, upper_bound)) for x in arr]

If you just wanted to set an upper bound, you could of course write arr = [min(x, upper_bound) for x in arr]. Or similarly if you just wanted a lower bound, you'd use max instead.

Here, I've just applied both operations, written together.

Edit: Here's a slightly more in-depth explanation:

Given an element x of the array (and assuming that your upper_bound is at least as big as your lower_bound!), you'll have one of three cases:

1. x < lower_bound
2. x > upper_bound
3. lower_bound <= x <= upper_bound.

In case 1, the max/min expression first evaluates to max(lower_bound, x), which then resolves to lower_bound.

In case 2, the expression first becomes max(lower_bound, upper_bound), which then becomes upper_bound.

In case 3, we get max(lower_bound, x) which resolves to just x.

In all three cases, the output is what we want.