I have an np array of a number single entry lists and I want to add 1 to each single entry lists

I have created the following array, called X:

array([[6.575],
[6.421],
[7.185],
[6.998],
[6.43 ],
[6.012],
[6.172],
[5.631],
[6.004],
[6.377],
[6.03 ]])

and I would like to create

array([[6.575, 1],
[6.421, 1],
[7.185, 1],
[6.998, 1],
[6.43, 1],
[6.012, 1],
[6.172, 1],
[5.631, 1],
[6.004, 1],
[6.377, 1],
[6.03, 1]])

I have tried X = np.array( [ [value,1] for value in X ] ) but I get the error:

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (506, 2) + inhomogeneous part.

Aside from ignorance I am not sure where I am going wrong. Any ideas?

Do not use list comprehensions/loops to work with numpy arrays. You should combine expand the scalar with broadcast_to and combine it to X with hstack:

out = np.hstack([X, np.broadcast_to(1, X.shape)])

Output:

array([[6.575, 1.   ],
       [6.421, 1.   ],
       [7.185, 1.   ],
       [6.998, 1.   ],
       [6.43 , 1.   ],
       [6.012, 1.   ],
       [6.172, 1.   ],
       [5.631, 1.   ],
       [6.004, 1.   ],
       [6.377, 1.   ],
       [6.03 , 1.   ]])

Now, just to answer the source of your error, this this because your intermediate list comprehension generates items of the form [array([6.575]), 1] instead of [6.575, 1]. The correct approach should have been:

np.array([[value, 1] for value in X[:, 0]])

# or
np.array([[*value, 1] for value in X])

But, again, do not do this in numpy.