shape is a tuple that gives you an indication of the number of dimensions in the array. So in your case, since the index value of Y.shape[0] is 0, your are working along the first dimension of …

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 …

Nov 30, 2017 · 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 …

Jan 7, 2018 · x[0].shape will give the Length of 1st row of an array. x.shape[0] will give the number of rows in an array. In your case it will give output 10. If you will type x.shape[1], it will …

Sep 9, 2013 · When reshaping an array, the new shape must contain the same number of elements as the old shape, meaning the products of the two shapes' dimensions must be …

Jul 21, 2018 · In this case your training set will be of the shape (n,x,y). The fourth dimension in your data is the number of channels (3, or RGB in this case). In your dataset the height and …

Oct 19, 2017 · P.S.: Note that tensor.shape is an alias to tensor.size(), though tensor.shape is an attribute of the tensor in question whereas tensor.size() is a function. Share Improve this answer

May 24, 2016 · So maybe df.shape[0] + df.shape[1] is slower than len(df.index) + len(df.columns). Still, performance-wise, the difference should be negligible except for a giant giant 2D …

The main problem was that you were using two different vectors to describe your groups resulting in two legends. using your group2 gives each group their own shape/colour. then you choose …

Jul 16, 2013 · The most convenient method is to create a macro button, which is accessible from your tabs (e.g., Home, Insert, etc.). This way, you just click on the shape, click the macro …