Transform array into a binary array

Question

I currently have a numpy array with the following data:

array([0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,
   1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,
   0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,
   1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,
   1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1,
   0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1,
   0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0])

I need to transform it into an array that looks as follows:

array([0., 1.],
      [0., 1.],
      [1., 0.],
      [1., 0.],
      [1., 0.],
      [0., 1.],
      [1., 0.],
      [1., 0.],
      [1., 0.],
      [1., 0.],
      ...
      [0., 1.])

I recognize the first step as obviously being a transform into a float, but I don't know where to start as far as the next step. Is there a map function that will work well? A lambda? A for loop? I feel like it would be simple if I already had it in a different array format, but I don't know how to conditionally add a dimension and then populate it with precisely the binary opposite of what is already there. Is all that based on an if-then conditional? Sorry, I'm just relatively new to Python so I don't know all the tools yet.

Looks like you want to reshape with fortran style ordering. Try a.reshape(-1, 2, order='F') — user3483203
– user3483203, Commented May 30, 2019 at 21:50

Paul Panzer · Accepted Answer · 2019-05-30 23:44:37Z

2

You can use advanced indexing into an appropriate template:

small_example = np.array([1,0,0,1,1,1,0,1])
(1-np.eye(2))[small_example]
# array([[1., 0.],
#        [0., 1.],
#        [0., 1.],
#        [1., 0.],
#        [1., 0.],
#        [1., 0.],
#        [0., 1.],
#        [1., 0.]])

The same idea can also be implemented using np.where:

np.where(small_example[:,None], *np.eye(2))
# array([[1., 0.],
#        [0., 1.],
#        [0., 1.],
#        [1., 0.],
#        [1., 0.],
#        [1., 0.],
#        [0., 1.],
#        [1., 0.]])

A more direct approach showing how to create column an row vectors, how to broadcast them together and how to cast the dtype:

(np.c_[small_example]^np.r_[:2]).astype(float)
# array([[1., 0.],
#        [0., 1.],
#        [0., 1.],
#        [1., 0.],
#        [1., 0.],
#        [1., 0.],
#        [0., 1.],
#        [1., 0.]])

We can also column stack the input and its "negative"; we use 1.0 to trigger type promotion:

np.c_[small_example,1.0-small_example]
# array([[1., 0.],
#        [0., 1.],
#        [0., 1.],
#        [1., 0.],
#        [1., 0.],
#        [1., 0.],
#        [0., 1.],
#        [1., 0.]])

edited May 30, 2019 at 23:44

answered May 30, 2019 at 23:12

Paul Panzer

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1 Comment

Yehuda Over a year ago

Brilliant use of np.eye(). I'm going to have to unpack the rest of the solutions as well, thanks.

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