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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
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
How do I create an empty array and then append to it in NumPy? That is the wrong mental model for using NumPy efficiently NumPy arrays are stored in contiguous blocks of memory To append rows or columns to an existing array, the entire array needs to be copied to a new block of memory, creating gaps for the new elements to be stored This is very inefficient if done repeatedly Instead of appending rows, allocate a suitably sized array, and then assign
Combine legends for color and shape into a single legend I'm creating a plot in ggplot from a 2 x 2 study design and would like to use 2 colors and 2 symbols to classify my 4 different treatment combinations Currently I have 2 legends, one for the colo
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