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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). Nov 30, 2017yourarray.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. Jul 21, 2018in 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]? Jan 7, 2018on 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.

Jan 23, 2020in 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? Oct 28, 2023for 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, 2018shape (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.

Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a loop) and then dies. So in line with the previous answers, df.shape is good if you need both dimensions, for a.