Suppose I have a numpy array
a = np.array([1, 100, 123, -400, 85, -98])
And I want to limit each value between -100 and 90. So basically, I want the numpy array to be like this:
a = np.array([1, 90, 90, -100, 85, -98])
I know this can be done through iterating over the numpy array, but is there any other efficient method to carry out this task?
There are several ways of doing so. First, using a numpy function as proposed by Sridhar Murali :
a = np.array([1, 100, 123, -400, 85, -98])
np.clip(a,-100,90)
Second, using numpy array comparison :
a = np.array([1, 100, 123, -400, 85, -98])
a[a>90] = 90
a[a<-100] = -100
Third, if a numpy is not required for the rest of your code, using list comprehension :
a = [1, 100, 123, -400, 85, -98]
a = [-100 if x<-100 else 90 if x>90 else x for x in a]
They all give the same result :
a = [1, 90, 90, -100, 85, -98]
As for coding style, I would prefer numpy comparison or list comprehension as they state clearly what is done, but it is up to you really. As for speed, with timeit.repeat on 100000 repetitions, I get on average, from the best to the worst :
Clearly, if an array is not necessary afterwards, list comprehension is the way to go. And if you need an array, direct comparison is almost twice more efficient that the clip function, while more readable.
clip if you report that the latter takes less time to execute? And no, list comprehensions are cool syntaxic sugar but not the way to go if you have large arrays where vectorization is key to gain massive speedups. In your timings you just got lucky because the arrays were very small to begin with.
np.clip is 250 times faster than the list comprehension for an array with one million elements. As a suggestion, you could run the benchmark for different sizes of the array and say for which ranges it is practical to use a list comprehension.I think the easiest way for you to get the result is using the clip function from numpy.
import numpy as np
a = np.array([1, 100, 123, -400, 85, -98])
np.clip(a,-100,90)
np.clip/nd.ndarray.clip