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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Quick Fix: check the .ndim property - if its 2, then the .shape property will work as you expect.
Reason Why: if the .ndim property is 2, then numpy reports a shape value that agrees with the convention. If the .ndim property is 1, then numpy just reports shape in a different way.
More talking: When you pass np.array a lists of lists, the .shape property will agree with standard notions of the dimensions of a matrix: (rows, columns).
If you pass np.array just a list, then numpy doesn't think it has a matrix on its hands, and reports the shape in a different way.
The question is: does numpy think it has a matrix, or does it think it has something else on its hands.