ndarray.flatten(order='C')
Return a copy of the array collapsed into one dimension.
>>> import numpy as np
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
numpy.ravel(a, order='C')
Return a contiguous flattened array.
>>> import numpy as np
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.ravel(x)
array([1, 2, 3, 4, 5, 6])
>>> x.reshape(-1)
array([1, 2, 3, 4, 5, 6])
>>> np.ravel(x, order='F')
array([1, 4, 2, 5, 3, 6])
numpy.stack(arrays, axis=0, out=None, *, dtype=None, casting='same_kind')
Join a sequence of arrays along a new axis.
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.stack((a, b))
array([[1, 2, 3],
[4, 5, 6]])
numpy.split(ary, indices_or_sections, axis=0)
Split an array into multiple sub-arrays as views into ary.
>>> import numpy as np
>>> x = np.arange(9.0)
>>> np.split(x, 3)
[array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
>>> x = np.arange(8.0)
>>> np.split(x, [3, 5, 6, 10])
[array([0., 1., 2.]),
array([3., 4.]),
array([5.]),
array([6., 7.]),
array([], dtype=float64)]