CODEWITHSUNDEEP
python-3.xnumpy
uday
uday

Reputation: 6733

numpy assignment doesn't work

Suppose I have the following numpy.array:

In[]: x
Out[]: 
array([[1, 2, 3, 4, 5],
       [5, 2, 4, 1, 5],
       [6, 7, 2, 5, 1]], dtype=int16)


In[]: y
Out[]: 
array([[-3, -4],
       [-4, -1]], dtype=int16)

I want to replace a sub array of x by y and tried the following:

In[]: x[[0,2]][:,[1,3]]= y

Ideally, I wanted this to happen:

In[]: x
Out[]: 
array([[1, -3, 3, -4, 5],
       [5, 2, 4, 1, 5],
       [6, -4, 2, -1, 1]], dtype=int16)

The assignment line doesn't give me any error, but when I check the output of x

 In[]: x

I find that x hasn't changed, i.e. the assignment didn't happen.

How can I make that assignment? Why did the assignment didn't happen?

Upvotes: 4

Views: 778

Answers (1)

YXD
YXD

Reputation: 32521

The the "fancy indexing" x[[0,2]][:,[1,3]] returns a copy of the data. Indexing with slices returns a view. The assignment does happen, but to a copy (actually a copy of a copy of...) of x.

Here we see that the indexing returns a copy:

>>> x[[0,2]]
array([[1, 2, 3, 4, 5],
       [6, 7, 2, 5, 1]], dtype=int16)
>>> x[[0,2]].base is x
False
>>> x[[0,2]][:, [1, 3]].base is x
False
>>>

Now you can use fancy indexing to set array values, but not when you nest the indexing.

You can use np.ix_ to generate the indices and perform the assignment:

>>> x[np.ix_([0, 2], [1, 3])]
array([[2, 4],
       [7, 5]], dtype=int16)
>>> np.ix_([0, 2], [1, 3])
(array([[0],
       [2]]), array([[1, 3]]))
>>> x[np.ix_([0, 2], [1, 3])] = y
>>> x
array([[ 1, -3,  3, -4,  5],
       [ 5,  2,  4,  1,  5],
       [ 6, -4,  2, -1,  1]], dtype=int16)
>>>

You can also make it work with broadcasted fancy indexing (if that's even the term) but it's not pretty

>>> x[[0, 2], np.array([1, 3])[..., None]] = y
>>> x
array([[ 1, -3,  3, -4,  5],
       [ 5,  2,  4,  1,  5],
       [ 6, -4,  2, -1,  1]], dtype=int16)

By the way, there is some interesting discussion at the moment on the NumPy Discussion mailing list on better support for "orthogonal" indexing so this may become easier in the future.

Upvotes: 5

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