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 ...
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. (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. in function calls).
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.
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
Still, performance-wise, the difference should be negligible except for a giant giant 2D dataframe. So in line with the previous answers, df.shape is good if you need both dimensions, for a single dimension, len() seems more appropriate conceptually. Looking at property vs method answers, it all points to usability and readability of code.
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.
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.
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
