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.

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 …

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 …

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 …

Sep 9, 2013 · The criterion to satisfy for providing the new shape is that 'The new shape should be compatible with the original shape' numpy allow us to give one of new shape parameter as …

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 = …

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 …

Jan 23, 2020 · In R graphics and ggplot2 we can specify the shape of the points. I am wondering what is the main difference between shape = 19, shape = 20 and shape = 16? Is it the size? …

Feb 14, 2022 · Currently, shape type information is reflected in ndarray.shape. However, most numpy functions that change the dimension or size of an array, however, don't necessarily …

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 …