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Shape is a tuple that gives you an indication of the number of dimensions in the array Shape of passed values is (x, ), indices imply (x, y) asked 12 years ago modified 7 years, 8 months ago viewed 60k times So in your case, since the index value of y.shape[0] is 0, your are working along the first dimension of your array.

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(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 I am wondering what is the main difference between shape = 19, shape = 20 and shape = 16 82 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()

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 handle different axes and sizes in typing As a result something like Arr[:] will lose the shape type information from arr.

I already know how to set the opacity of the background image but i need to set the opacity of my shape object In my android app, i have it like this And i want to make this black area a bit A shape tuple (integers), not including the batch size

Elements of this tuple can be none

'none' elements represent dimensions where the shape is not known. 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 large fraction of readers will be printing black and white copies of the paper, and so you also want to include a visual cue that isn't dependent on color. 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.

In r graphics and ggplot2 we can specify the shape of the points