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empty Return a new array of given shape and type. np.empty((3,3)) >>> array([[-1., 0., 5.], [12., 18., 24.], [35., 60., inf]]) where Return elements depending on condition. ar=np.arange(1,10) np.where(ar>5) >>> (array([5, 6, 7, 8]),) allclose Returns True if two arrays are element-wise equal. np.allclose([2,3],[2,3],equal_nan=True) >>> True equal_nan : Whether to compare NaN’s as equal. If True,..

array Create an array. dtype="object" a_object = np.array([1, 0.1, 'one'], dtype=object) print(a_object) print(a_object.dtype) >>> [1 0.1 'one'] >>> object print(type(a_object[0])) print(type(a_object[1])) print(type(a_object[2])) >>> >>> >>> It stores pointers to Python objects. Since the data of each element itself is stored in each memory area, it can have multiple data types (pointers to) in..