How do I subtract and divide a 2D array and a 1D array in Python Numpy?

Question

I have a 2D numpy array defined A, for example. I want to transform it into another 2D arrays according to the following statements:

B = A - mean(A), the mean by the second axis
C = B / mean(A)

An example:

>>> import numpy as np
>>> A = np.array([[1, 2, 3], [4, 6, 8]])
>>> A
array([[1, 2, 3],
       [4, 6, 8]])
>>> M = np.mean(A, axis=1)
>>> M
array([ 2.,  6.])
>>> B = ... # ???
>>> B
array([[-1., 0., 1.],
       [-2., 0., 2.]])
>>> C = ... # ???
>>> C
array([[-0.5, 0., 0.5],
       [-0.33333333, 0., 0.33333333]])

Gareth McCaughan · Accepted Answer · 2016-04-28 12:45:01Z

3

Annoyingly, numpy.mean(axis=...) gives you an array where the relevant axis has been deleted rather than set to size 1. So when you apply this to a 2x3 array with axis=1, you get a (rank-1) array of size 2 rather than the 2x1 array you really want.

You can fix this up by supplying the keepdims argument to numpy.mean:

M = np.mean(A, axis=1, keepdims=True)

If that hadn't existed, an alternative would have been to use reshape.

answered Apr 28, 2016 at 12:45

Gareth McCaughan

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P. Camilleri · Accepted Answer · 2016-04-28 12:50:13Z

3

Gareht McCaughan's solution is more elegant, but in the case keepdims did not exist, you could add a new axis to M:

B = A - M[:, None]

(M[:, None].shape is (2, 1), so broadcasting happens)

answered Apr 28, 2016 at 12:50

P. Camilleri

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armatita · Accepted Answer · 2016-04-28 12:49:19Z

1

You can use the functions subtract and divide from numpy. Solving your example:

import numpy as np
A = np.array([[1, 2, 3], [4, 6, 8]])
M = np.mean(A, axis=1)
B = np.subtract(A.T,M).T
C = np.divide(B.T,M).T
print(B)
print(C)

, results in:

[[-1.  0.  1.]
 [-2.  0.  2.]]
[[-0.5         0.          0.5       ]
 [-0.33333333  0.          0.33333333]]

answered Apr 28, 2016 at 12:49

armatita

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