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The shape attribute for numpy arrays returns the dimensions of the array Why doesn't pyspark dataframe simply store the shape values like pandas dataframe does with.shape If y has n rows and m columns, then y.shape is (n,m)
(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 Currently i have 2 legends, one for the colo. 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. 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.
But isn't the input_shape defined as (sample_size,timestep, features) That's tensorflow site mentions about input_shape. For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc. For example the doc says units specify the output shape of a layer.
That is the wrong mental model for using numpy efficiently
Numpy arrays are stored in contiguous blocks of memory To append rows or columns to an existing array, the entire array needs to be copied to a new block of memory, creating gaps for the new elements to be stored This is very inefficient if done repeatedly Instead of appending rows, allocate a suitably sized array, and then assign.
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
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