Selecting the last values of a Numpy array

Question

I am doing some basic linear algebra solving, and I'm using np.linalg.solve() to do so. np.linalg.solve() requires that b consist of the right side of the equation, so with an augmented matrix, the augmentation (ahem) needs to be removed. Right now the method I'm using to do that is as follows:

np_exercise1 = np.array([[1,5,7],[-2,-7,-5]])
a = np_exercise1.T[0:2].T
b = np_exercise1.T[2]
solution1 = np.linalg.solve(a,b)
print('x1 = {}\nx2 = {}'.format(solution1[0],solution1[1]))

Is there a more elegant way to do this than than a double-transpose of the matrix?

nicoring · Accepted Answer · 2019-12-08 22:44:29Z

1

You can do multidimensional indexing in numpy, where : selects all the data in a dimension:

>>> np_exercise1[:, 0:2]
array([[ 1,  5],
       [-2, -7]])
>>> np_exercise1[:, 2]
array([ 7, -5])

answered Dec 8, 2019 at 22:44

nicoring

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