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


Returns: 

See also
compress
ndarray.take
take_along_axis
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 1d 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)
>>> 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]])
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.take.html