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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 your array. 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 yourarray.ndim or np.ndim() 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. 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 The gist for python is found here reproducing the gist from 3 From onnx import shape_inference inferred_model = shape_inference.infer_shapes(original_model) and find the shape info in inferred_model.graph.value_info You can also use netron or from github to have a visual representation of that information. 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. Is it possible to add a drop shadow to a custom shape in android After looking through the documentation, i only see a way to apply a text shadow I've tried this with no luck 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] It is often appropriate to have redundant shape/color group definitions In many scientific publications, color is the most visually effective way to distinguish groups, but you also know that a large fraction of readers will be printing black and white copies of the paper, and so you also want to include a visual cue that isn't dependent on color.