I have initialized this empty 2d np.array
inputs = np.empty((300, 2), int)
And I am attempting to append a 2d row to it as such
inputs = np.append(inputs, np.array([1,2]), axis=0)
But Im getting
ValueError: all the input arrays must have same number of dimensions
And Numpy thinks it's a 2 row 0 dimensional object (transpose of 2d)
np.array([1, 2]).shape
(2,)
Where have I gone wrong?
To add a row to a (300,2) shape array, you need a (1,2) shape array. Note the matching 2nd dimension.
np.array([[1,2]]) works. So does np.array([1,2])[None, :] and np.atleast_2d([1,2]).
I encourage the use of np.concatenate. It forces you to think more carefully about the dimensions.
Do you really want to start with np.empty? Look at its values. They are random, and probably large.
@Divakar suggests np.row_stack. That puzzled me a bit, until I checked and found that it is just another name for np.vstack. That function passes all inputs through np.atleast_2d before doing np.concatenate. So ultimately the same solution - turn the (2,) array into a (1,2)
Numpy requires double brackets to declare an array literal, so
np.array([1,2])
needs to be
np.array([[1,2]])
(2,), the other (1,2), 1d and 2d. I don't know what you mean by array literal.
[1, 2] into a numpy, you [sometimes] need an extra set of brackets"If you intend to append that as the last row into inputs, you can just simply use np.row_stack -
np.row_stack((inputs,np.array([1,2])))
Please note this np.array([1,2]) is a 1D array.
You can even pass it a 2D row version for the same result -
np.row_stack((inputs,np.array([[1,2]])))
np.array([[1, 2]]).shape(2,)is a 1d array. It's not a transpose of anything else.