# Map 1d numpy array to 2d array based on element-wise function

I have a numpy array, it has `shape=(10000,)`. Here are the first 5 entries:

``````labels = data[:, 0]
print(labels.shape)
print(labels[0:5])

# prints
# (100000,)
# [1. 1. 1. 0. 1.]
``````

Every entry is either 0 or 1. I would like to map this to a 2d array, by an element wise operation which maps

``````0 -> [1, 0]
1 -> [0, 1]
``````

How do I do this? I tried

``````labels = np.apply_along_axis(lambda x: [1, 0] if x[0] == 0 else [0, 1], 0, data[:, 0])
``````

but that did not seem to work.

pythonarraysnumpy

answered 6 months ago Shaido #1

You could try the following

``````labels = np.array([1,1,1,0,1])
np.eye(np.max(labels) + 1)[labels]
``````

which gives:

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

answered 6 months ago Tai #2

This method does xor on the original array and stack two arrays together.

``````labels = np.random.randint(0,2, 10000)
# array([0, 0, 1, ..., 1, 1, 0])
np.vstack([(~labels.astype(bool)).astype(int), labels])
array([[1, 1, 0, ..., 0, 0, 1],
[0, 0, 1, ..., 1, 1, 0]])
``````

answered 6 months ago hpaulj #3

``````In [435]: ref = np.array([[1,0],[0,1]])
In [436]: index = np.array([1.,1.,1.,0.,1.])
``````

``````In [437]: ref[index,:]
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-437-d50c95668d6c> in <module>()
----> 1 ref[index,:]

IndexError: arrays used as indices must be of integer (or boolean) type
``````

Index with integers, select rows from `ref` depending on the `index` value:

``````In [438]: ref[index.astype(int),:]
Out[438]:
array([[0, 1],
[0, 1],
[0, 1],
[1, 0],
[0, 1]])
``````

This is a case where `choose` could be used, but it's pickier about array shapes than the above indexing:

``````In [440]: np.choose(index.astype(int)[:,None],[[1,0],[0,1]])
Out[440]:
array([[0, 1],
[0, 1],
[0, 1],
[1, 0],
[0, 1]])
``````

or with only 2 choices that convert to boolean, `where`:

``````In [443]: np.where(index.astype(bool)[:,None],[0,1],[1,0])
Out[443]:
array([[0, 1],
[0, 1],
[0, 1],
[1, 0],
[0, 1]])
``````