The
numpy.one_like() function returns an array of given shape and type as a given array, with ones.
Syntax: numpy.ones_like(array, dtype = None, order = 'K', subok = True)
Parameters : array : 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-wise 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(byDefault)] Data type of returned array.
Returns :
ndarray of ones having given shape, order and datatype.
Output:
Original array :
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]
Matrix b :
[[ 1. 1.]
[ 1. 1.]
[ 1. 1.]
[ 1. 1.]
[ 1. 1.]]
Matrix c :
[1 1 1 1 1 1 1 1]
Also, these codes won’t run on online-ID. Please run them on your systems to explore the working
