numpy.take¶

numpy.take(a, indices, axis=None, out=None, mode='raise')[源代码]

Take elements from an array along an axis.

When axis is not None, this function does the same thing as “fancy” indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. A call such as np.take(arr, indices, axis=3) is equivalent to arr[:,:,:,indices,...].

Explained without fancy indexing, this is equivalent to the following use of ndindex, which sets each of ii, jj, and kk to a tuple of indices:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
Nj = indices.shape
for ii in ndindex(Ni):
for jj in ndindex(Nj):
for kk in ndindex(Nk):
out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
Parameters: a : array_like (Ni…, M, Nk…) The source array. indices : array_like (Nj…) The indices of the values to extract. 1.8.0 新版功能. Also allow scalars for indices. axis : int, optional The axis over which to select values. By default, the flattened input array is used. out : ndarray, optional (Ni…, Nj…, Nk…) If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. mode : {‘raise’, ‘wrap’, ‘clip’}, optional Specifies how out-of-bounds indices will behave. ‘raise’ – raise an error (default) ‘wrap’ – wrap around ‘clip’ – clip to the range ‘clip’ mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers. out : ndarray (Ni…, Nj…, Nk…) The returned array has the same type as a.

compress
Take elements using a boolean mask
ndarray.take
equivalent method
take_along_axis
Take elements by matching the array and the index arrays

Notes

By eliminating the inner loop in the description above, and using s_ to build simple slice objects, take can be expressed in terms of applying fancy indexing to each 1-d slice:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nj):
out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]

For this reason, it is equivalent to (but faster than) the following use of apply_along_axis:

out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)

Examples

>>> a = [4, 3, 5, 7, 6, 8]
>>> indices = [0, 1, 4]
>>> np.take(a, indices)
array([4, 3, 6])

In this example if a is an ndarray, “fancy” indexing can be used.

>>> a = np.array(a)
>>> a[indices]
array([4, 3, 6])

If indices is not one dimensional, the output also has these dimensions.

>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
[5, 7]])