numpy-ndarray

import numpy as np y = np.array(((1,2,3),(4,5,6),(7,8,9))) OUTPUT: print(y.flatten()) [1 2 3 4 5 6 7 8 9] print(y.ravel()) [1 2 3 4 5 6 7 8 9] Both function return the same list. Then what is the need of two different functions performing same job. IanH The current API is that: flatten always returns a copy. ravel returns a view of the original array whenever possible. This isn't visible in the printed output, but if you modify the array returned by ravel, it may modify the entries in the original array. If you modify the entries in an array returned from flatten this will never happen. ravel

Let's say I have a 1d numpy array a = array([1,0,3]) I would like to encode this as a 2d 1-hot array b = array([[0,1,0,0], [1,0,0,0], [0,0,0,1]]) Is there a quick way to do this? Quicker than just looping over a to set elements of b , that is. Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing: >>> a = np.array([1, 0, 3]) >>> b = np.zeros((3, 4)) >>> b[np.arange(3), a] = 1 >>> b array([[ 0., 1., 0., 0.], [ 1., 0., 0., 0.], [ 0., 0., 0., 1.]]) >>> values = [1, 0, 3] >>> n_values = np.max(values) + 1 >>> np