numpy.zeros_like(array, dtype = None, order = ‘K’, subok = True) : Return array of given shape and type as given array, with zeros,
arrray : array_like input subok : [optional, boolean]If true, then newly created array will be sub-class of array; otherwise, a base-class array order : C_contiguous or F_contiguous C-contiguous order in memory(last index varies the fastest) C order means that operating row-rise on the array will be slightly quicker FORTRAN-contiguous order in memory (first index varies the fastest). F order means that column-wise operations will be faster. dtype : [optional, float(byDeafult)] Data type of returned array.
ndarray of zeros having given shape, order and datatype.
Code 1 :
Original array : [[0 1] [2 3] [4 5] [6 7] [8 9]] Matrix b : [[ 0. 0.] [ 0. 0.] [ 0. 0.] [ 0. 0.] [ 0. 0.]] Matrix c : [0 0 0 0 0 0 0 0]
Code 2 :
Original array : [[0 1] [2 3] [4 5] [6 7] [8 9]] Matrix : [[ 0. 0.] [ 0. 0.]] Matrix : [ 0. 0. 0. 0. 0. 0. 0. 0.]
Also, these codes won’t run on online-ID. Please run them on your systems to explore the working
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