Numpy’s clip function is a handy function that brings all data in a series into a range. For example, in machine learning, it is common to have activation functions that take a continuous range of values and bring them to a range like 0 to 1, or -1 to 1.
In my case, I was working on some quality control charts that have control limits. In this case, control limits could be calculated to be below 0, but should never be. I was working in R. R has a function max() that can take two values and return the max. So by applying that function over a series, you make make sure each value was at least 0. But it felt a bit cumbersome compared to clip. min() and max() are not vectorized in R. That makes sense, because you may want the max or min of the entire series, so it makes sense that you can pass a series or many values, and get a single answer.
Note that in my use case, the second value is a scalar. But it could be a vector of the same size.
I discovered the pmin and pmax functions which can clip a single bound, or be combined to approximate the clip function. Here’s a gist showing plots of how this works:
To see the plotted output, click here.
