Indexing gives view of data but copy of axis

[kwgoodman]

Non-fancy indexing of a numpy array returns a view. A view of the data of a datarray is returned:

>> x = DataArray([[1,2],[3,4]], [('row', ['r1', 'r2']), ('col', ['c1', 'c2'])])
>> y = x[0]

>> y[0] = 9
>> x[0,0]
   9

But a copy of the axes is returned:

>> y.axes[0].ticks[0] = 'X'
>> x.axes[0].ticks[0]
   'r1'

Is that what we want?

[rspeer]

I see two ways to fix this:

• Make ticks immutable
• Store ticks in a list subclass with copy-on-write semantics

Copying the ticks every time would be far too slow for NumPy.

[kwgoodman]

Ticks are one example. But the Axis object contains other things like index and name. If we went the immutable route, then the entire Axis object would have to be immutable. At the moment we can't do that since we have to be able to change the index, for example, for slicing.

pandas went the immutable route. One advantage is that the output of a binary operation can return a view of the Axis since axes (Index in pandas) is immutable. That's fast.

But I like to be able to change the ticks in my own use.