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The shape attribute for numpy arrays returns the dimensions of the array I created a custom stencil in my shapes, right clicked it and selected edit s. If y has n rows and m columns, then y.shape is (n,m)
(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 Background i want to create a reusable shape in visio (visio 365 desktop) with certain data attached X.shape[0] gives the first element in that tuple, which is 10
Here's a demo with some smaller numbers, which should hopefully be easier to understand.
Donuts (hollow circles) are also intriguing What would it take to build one of these shapes and incorporate it fully into ggplot's machinery so that it just works whenever a user says shape = xxx in a ggplot call Ideally, any shape added would have separate stroke color and interior fill color aesthetics. 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 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]
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 more obvious in the arguments names. Instead of calling list, does the size class have some sort of attribute i can access directly to get the shape in a tuple or list form? Why doesn't pyspark dataframe simply store the shape values like pandas dataframe does with.shape
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