ndarray.getitem¶

ndarray.__getitem__(self, key)¶

Return self[key].

Returns a sliced view of this array if the elements fetched are contiguous in memory; otherwise, returns a newly created NDArray. This functions supports advanced indexing defined in the following reference with some restrictions. Boolean indexing is supported only for a single boolean ndarray as a key. Mixing boolean ndarray with other index types is not supported in advanced indexing.

For basic indexing, i.e., if key consists only of integers, slice, Ellipsis (...) and None, a mutable view is returned that shares memory with this array if the accessed portion is contiguous in memory. Otherwise, a newly created ndarray is returned.

This functions supports advanced indexing as defined in the NumPy advanced indexing documentation.

Parameters:	key : int, slice, list, np.ndarray, mx.np.ndarray, or tuple of all previous types Indexing key.

Examples

The default is to give explicit indices for all axes:

>>> x = np.arange(6).reshape(2, 3)
>>> x
array([[0., 1., 2.],
       [3., 4., 5.]])
>>> x[0, :2]
array([0., 1.])
>>> x[:, :-1]
array([[0., 1.],
       [3., 4.]])

If fewer indices are given, they are automatically supplemented by an appropriate number of slice(None) (“:”) to the right. For instance, a single integer indexes along the first axis:

>>> x[0]
array([0., 1., 2.])
>>> x[1:]
array([[3., 4., 5.]])

To omit a range of axes that should be kept as-is, an Ellipsis (“...”) can be used:

>>> x = np.arange(16).reshape(2, 2, 2, 2)
>>> x[0, ..., 1]
array([[1., 3.],
       [5., 7.]])
>>> x[0, :, :, 1]  # equivalent
array([[1., 3.],
       [5., 7.]])

New axes of length 1 can be created by inserting None (numpy.newaxis) in the index:

>>> x = np.arange(6).reshape(2, 3)
>>> x[None, :, :]
array([[[0., 1., 2.],
        [3., 4., 5.]]])
>>> x[None, :, :].shape
(1, 2, 3)

If the indexed portion of the array is contiguous in memory, no data is copied. Instead, a shared-memory view of the original array is returned, and changes to that view affect the original array:

>>> x = np.arange(8).reshape(2, 2, 2)
>>> y = x[0]  # contiguous
>>> y
array([[0., 1.],
       [2., 3.]])
>>> y[:] = -1
>>> x
array([[[-1., -1.],
        [-1., -1.]],
       [[ 4.,  5.],
        [ 6.,  7.]]])
>>> x = np.arange(8).reshape(2, 2, 2)
>>> y = x[1, :1, :]  # contiguous
>>> y
array([[4., 5.]])
>>> y[:] = -1
>>> x
array([[[ 0.,  1.],
        [ 2.,  3.]],
       [[-1., -1.],
        [ 6.,  7.]]])
>>> x = np.arange(0, 8).reshape(2, 2, 2)
>>> y = x[:, :, 1]  # not contiguous
>>> y
array([[1., 3.],
       [5., 7.]])
>>> y[:] = -1
>>> x
array([[[0., 1.],
        [2., 3.]],
       [[4., 5.],
        [6., 7.]]])

If the indexing key contains list, numpy.ndarray or NDArray objects, advanced indexing is triggered, which always returns a copy:

>>> x = np.arange(8).reshape(2, 2, 2)
>>> x[[0, 1]]
array([[[0., 1.],
        [2., 3.]],
       [[4., 5.],
        [6., 7.]]])
>>> x[[0, 1], :]  # equivalent
array([[[0., 1.],
        [2., 3.]],
       [[4., 5.],
        [6., 7.]]])
>>> y = np.array([0, 1], dtype='int32')
>>> x[1:, y]
array([[[4., 5.],
        [6., 7.]]])
>>> y = np.array([0, 1], dtype='int32')
>>> x[1:, y]
array([[[4., 5.],
        [6., 7.]]])

Get negative elements in an ndarray through boolean array indexing >>> x = np.array([1., -1., -2., 3]) >>> x[x < 0] array([-1., -2.])

ndarray.__getitem__¶

ndarray.getitem¶