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As array size gets close to 5,000,000, Numpy gets around 120 times faster. As the array size increases, Numpy is able to execute more parallel operations and making computation faster.
In NumPy, you filter an array using a boolean index list. A boolean index list is a list of booleans corresponding to indexes in the array. If the value at an index is True that element is contained in the filtered array, if the value at that index is False that element is excluded from the filtered array.
Appending to numpy arrays is very inefficient. This is because the interpreter needs to find and assign memory for the entire array at every single step. Depending on the application, there are much better strategies. If you know the length in advance, it is best to pre-allocate the array using a function like np.
NumPy doesn't do this, so the challenge is to present the same interface as NumPy without explicitly using lazy evaluation.
You could just vectorize the function and then apply it directly to a Numpy array each time you need it:
import numpy as np
def f(x):
return x * x + 3 * x - 2 if x > 0 else x * 5 + 8
f = np.vectorize(f) # or use a different name if you want to keep the original f
result_array = f(A) # if A is your Numpy array
It's probably better to specify an explicit output type directly when vectorizing:
f = np.vectorize(f, otypes=[np.float])
A similar question is: Mapping a NumPy array in place. If you can find a ufunc for your f(), then you should use the out parameter.
If you are working with numbers and f(A(i,j)) = f(A(j,i)), you could use scipy.spatial.distance.cdist defining f as a distance between A(i) and A(j).
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