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 np.ndim(). …

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 …

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 …

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 indeed be a bit …

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 know how to …

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? This post might …

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 …

May 10, 2016 · It is often appropriate to have redundant shape/color group definitions. In many scientific publications, color is the most visually effective way to distinguish groups, but you also know that a …