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It seems that numpy.take(array, indices) and numpy.choose(indices, array) return the same thing: a subset of array indexed by indices.
Are there only subtle differences between the two, or am I missing something more important? And is there a reason to prefer one over the other?
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The numpy take() function takes elements from an array along an axis. The take() function does the same thing as “fancy” indexing (indexing arrays using arrays); however, it can be easier to use if you need items along the given axis.
A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. The Python core library provided Lists.
Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once. For example, consider the following array: import numpy as np rand = np. random. RandomState(42) x = rand.
numpy.take(array, indices) and numpy.choose(indices, array) behave similarly on 1-D arrays, but this is just coincidence. As pointed out by jonrsharpe, they behave differently on higher-dimensional arrays.
numpy.take(array, indices) picks out elements from a flattened version of array. (The resulting elements are of course not necessarily from the same row.)
For example,
numpy.take([[1, 2], [3, 4]], [0, 3])
returns
array([1, 4])
numpy.choose(indices, set_of_arrays) plucks out element 0 from array indices[0], element 1 from array indices[1], element 2 from array indices[2], and so on. (Here, array is actually a set of arrays.)
For example
numpy.choose([0, 1, 0, 0], [[1, 2, 3, 4], [4, 5, 6, 7]])
returns
array([1, 5, 3, 4])
because element 0 comes from array 0, element 1 comes from array 1, element 2 comes from array 0, and element 3 comes from array 0.
These descriptions are simplified – full descriptions can be found here: numpy.take, numpy.choose. For example, numpy.take and numpy.choose behave similarly when indices and array are 1-D because numpy.choose first broadcasts array.
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