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- What does . shape [] do in for i in range (Y. shape [0])?
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
- arrays - what does numpy ndarray shape do? - Stack Overflow
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() (i e it gives the n of the ndarray since all arrays in NumPy are just n-dimensional arrays (shortly called as ndarray s)) For a 1D array, the shape would be (n,) where n is the number of elements in your array
- python - x. shape [0] vs x [0]. shape in NumPy - Stack Overflow
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 smaller numbers, which should hopefully be easier to understand
- android - How to set shapes opacity? - Stack Overflow
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
- numpy: size vs. shape in function arguments? - Stack Overflow
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
- PyTorch: How to get the shape of a Tensor as a list of int
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?
- python - Explaining the differences between dim, shape, rank, dimension . . .
I'm new to python and numpy in general I read several tutorials and still so confused between the differences in dim, ranks, shape, aixes and dimensions My mind seems to be stuck at the matrix
- How to find the size or shape of a DataFrame in PySpark?
Why doesn't Pyspark Dataframe simply store the shape values like pandas dataframe does with shape? Having to call count seems incredibly resource-intensive for such a common and simple operation
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