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 …

Nov 30, 2017 · 82 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 …

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 …

Sep 9, 2013 · It is quite simple, -1 means 'whatever it takes' to flatten. So, in the above example, a.reshape (2,-1) would mean 2*4, a.reshape (4,-1) would mean 4*2, a.reshape (2,2,-1) would …

Jan 7, 2018 · 10 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 …

Jul 21, 2018 · 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]? Code: m_train = …

May 24, 2016 · 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 …

Jan 9, 2014 · list object in python does not have 'shape' attribute because 'shape' implies that all the columns (or rows) have equal length along certain dimension. Let's say list variable a has …

Feb 1, 2021 · The shape property of the img object evidently is a list which contains some image data, the first two elements of which are here being copied into variables height and width.

Oct 22, 2018 · 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 …