The shape attribute for numpy arrays returns the dimensions of the array. If Y has n rows and m columns, then Y.shape is (n,m). So Y.shape[0] is n.
python - What does .shape [] do in "for i in range (Y.shape [0 ...
Shape n, expresses the shape of a 1D array with n items, and n, 1 the shape of a n-row x 1-column array. (R,) and (R,1) just add (useless) parentheses but still express respectively 1D and 2D array shapes, Parentheses around a tuple force the evaluation order and prevent it to be read as a list of values (e.g. in function calls).
On the other hand, x.shape is a 2-tuple which represents the shape of x, which in this case is (10, 1024). x.shape[0] gives the first element in that tuple, which is 10. Here's a demo with some smaller numbers, which should hopefully be easier to understand.
python - x.shape [0] vs x [0].shape in NumPy - Stack Overflow
In python shape [0] returns the dimension but in this code it is returning total number of set. Please can someone tell me work of shape [0] and shape [1]? Code: m_train = train_set_x_orig.shape [0]
python - What does 'range (y.shape [1])' mean in "for i in range ...
The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (200, 2) + inhomogeneous part. Concerned codelines are: ... Basically, the code iterates through a collections of images and performs some operations on such images that outputs the (1x4) vector. The image and its operated-output are then added to the dataset.