Python - Fast way to split positive integer into array of ones

Question

I have an array of the shape (100000, 1) with each element in an array of type positive integer and not greater than 6. My goal is to convert each element into 1's and place these 1's in the new matrix of shape (100000, 6). For example, Input X = np.array([[6], [2], [1], ..., [5], [4], [3]]) # shape of X is (100000, 1) Output Y = np.array([[1, 1, 1, 1, 1, 1], [1, 1, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0], [ ... ], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]]) # shape of Y is (100000, 6) Is there any method that can achieve this without looping? Any help would be appreciated. Select the correct answer from above options

Answer 1

One way to achive is by using the numpy.flip with cumsum: max_ = 6 np.flip(np.flip(np.eye(max_)[X.ravel()-1], 1).cumsum(1), 1) Output: array([[1., 1., 1., 1., 1., 1.], [1., 1., 0., 0., 0., 0.], [1., 0., 0., 0., 0., 0.], [1., 1., 1., 1., 1., 0.], [1., 1., 1., 1., 0., 0.], [1., 1., 1., 0., 0., 0.]]) Benchmark with the value 100k: x_large = np.random.randint(1, 7, 100000) max_ = 6 %timeit np.flip(np.flip(np.eye(max_)[x_large.ravel()-1], 1).cumsum(1), 1) # 6.71 ms ± 68.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) If you are a beginner and want to know more about Python the do check out the data science with python course