DEPR: Series.view

[jreback]

we do not fully control memory in pandas currently; its defered to numpy, so a view() rarely makes sense, esp since we will change the dtype appropriate upon setting, breaking the view association.

In [35]: s = pd.Series([1, 2, 3])

In [36]: s_view = s.view()

In [39]: s_view
Out[39]: 
0    4.940656e-324
1    9.881313e-324
2    1.482197e-323
dtype: float64

we should just remove .view() entirely.

[jorisvandenbossche]

Didn't see this one yet, but just opened: #20281.
Deprecating is also a solution, but just fixing the behaviour when no dtype is specified (the one you show above) is also an option.

But I agree it is hardly useful given the constraint on keeping the same number of elements.

[villasv]

The only meaningful use case I could think of is the one I chose for the example in the docstring: converting unsigned int from/to signed int. Basically every other byte conversion is very unlikely to be useful for regular pandas users.

Contrived scenario:

Let's say I have a really big Series of int8 integers that barely fits in memory (which is basically why I'm trying to use int8 instead of int64). I then realize that actually int8 isn't enough because I have some data points in the 127~255 range, and because none of the data points are negative I could have been using uint8 all along. Creating a uint8 view would be my salvation - assuming that for some reason I don't want to simply redo the computation steps this time using uint8 from the beginning.