numpy.shape gives inconsistent responses - why?

Question

Why does the program

import numpy as np

c = np.array([1,2])
print(c.shape)
d = np.array([[1],[2]]).transpose()
print(d.shape)

give

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Answer 1

len(c.shape) is the "depth" of the array.

For c, the array is just a list (a vector), the depth is 1.
For d, the array is a list of lists, the depth is 2.

Note:

c.transpose()
# array([1, 2])

which is not d, so this behaviour is not inconsistent.

dt = d.transpose()
# array([[1],
#        [2]])
dt.shape # (2,1)