CODEWITHSUNDEEP
pythonpython-3.xnumpy
user8682794
user8682794

Reputation:

Subtracting columns from a numpy array

This question is a follow-up of a previous post of mine:

Multiply each column of a numpy array with each value of another array.

Suppose i have the following arrays:

In [252]: k
Out[252]: 
array([[200, 800, 400, 1600],
       [400, 1000, 800, 2000],
       [600, 1200, 1200,2400]])

In [271]: f = np.array([[100,50],[200,100],[300,200]])

In [272]: f
Out[272]: 
array([[100,  50],
       [200, 100],
       [300, 200]])

How can i subtract f from k to obtain the following?

In [252]: g
Out[252]: 
array([[100, 750, 300, 1550],
       [200, 900, 600, 1900],
       [300, 1000, 900,2200]])

Ideally, i would like to make the subtraction in as fewer steps as possible and in concert with the solution provided in my other post, but any solution welcome.

Upvotes: 2

Views: 3427

Answers (2)

Daniel
Daniel

Reputation: 42748

You can reshape k, to fit f in two dimensions, and use broadcasting:

>>> g = (k.reshape(f.shape[0], -1, f.shape[1]) - f[:, None, :]).reshape(k.shape)

array([[ 100,  750,  300, 1550],
       [ 200,  900,  600, 1900],
       [ 300, 1000,  900, 2200]])

Upvotes: 0

ely
ely

Reputation: 77404

You can use np.tile, like this:

In [1]: k - np.tile(f, (1, 2))
Out[1]: 
array([[ 100,  750,  300, 1550],
       [ 200,  900,  600, 1900],
       [ 300, 1000,  900, 2200]])

Also, if you happen to know for sure that each dimension of f evenly divides the corresponding dimension of k (which I assume you must, for your desired subtraction to make sense), then you could generalize it slightly:

In [2]: k - np.tile(f, np.array(k.shape) // np.array(f.shape))
Out[2]: 
array([[ 100,  750,  300, 1550],
       [ 200,  900,  600, 1900],
       [ 300, 1000,  900, 2200]])

Upvotes: 1

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