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Shape is a tuple that gives you an indication of the number of dimensions in the array For example the doc says units specify the output shape of a layer. So in your case, since the index value of y.shape[0] is 0, your are working along the first dimension of your 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 For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc. 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()

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. You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines that an unspecified number of rows of shape (128, 128, 3) of type float32 will be fed into the graph A placeholder does not hold state and merely defines the type and shape of the data to flow.

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. 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. 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]

8 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 following properties:

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