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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. 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. Nov 30, 2017 · yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; And you can get the (number of) dimensions of your array using yourarray.ndim or np.ndim().. Jul 21, 2018 · 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] Jan 7, 2018 · 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.

Oct 28, 2023 · For example on the this screenshot I have to the left a imported SVG and on the right a regular Draw.io shape. You can see that for the SVG, I can only edit Fill .cls-1 or Line .cls-1, but for. Oct 22, 2018 · Shape (in the numpy context) seems to me the better option for an argument name. The actual relation between the two is size = np.prod(shape) so the distinction should indeed be a bit. Dec 1, 2016 · train.shape[0] python returned 1467 - an integer Curious how Pandas handles these two different inputs, and why they are different. Is this a specific feature, or just a quirk? May 24, 2016 · Still, performance-wise, the difference should be negligible except for a giant giant 2D dataframe. So in line with the previous answers, df.shape is good if you need both dimensions, for a.