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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 # 1D array - shape (3,)arr_1d = np.array([1, 2, 3])# 2D column vector - shape (3, 1)arr_2d_col = np.array([[1], [2], [3]])# 2D row vector - shape (1, 3) arr_2d_row = np.array([[1, 2, 3]]) In here, while (3, 1) means "2 dimensions: 3 rows, 1 column", it is shown as (3, ) in 1D array. That is, it represents a 1D array with 3 elements, not a 2D array with rows and columns. That is, for a 1D ..