The numpy.full_like() function return a new array with the same shape and type as a given array.
numpy.full_like(a, fill_value, dtype = None, order = 'K', subok = True)
shape : Number of rows order : C_contiguous or F_contiguous dtype : [optional, float(by Default )] Data type of returned array. subok : [bool, optional] to make subclass of a or not
x before full_like : [[0 1 2 3 4] [5 6 7 8 9]] x after full_like : [[10 10 10 10 10] [10 10 10 10 10]] y before full_like : [[0 1 2 3 4] [5 6 7 8 9]] y after full_like : [[ 0.01 0.01 0.01 0.01 0.01] [ 0.01 0.01 0.01 0.01 0.01]]
These codes won’t run on online-ID. Please run them on your systems to explore the working
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