Hi all,
in https://github.com/numpy/numpy/pull/15925 I propose to deprecate
promotion of strings and numbers. I have to double check whether this
has a large effect on pandas, but it currently seems to me that it will
be reasonable.
This means that `np.promote_types("S", "int8")`, etc. will lead to an
error instead of returning `"S4"`. For the user, I believe the two
main visible changes are that:
np.array(["string", 0])
will stop creating a string array and return either an `object` array
or give an error (object array would be the default currently).
Another larger visible change will be code such as:
np.concatenate([np.array(["string"]), np.array([2])])
will result in an error instead of returning a string array. (Users
will have to cast manually here.)
The alternative is to return an object array also for the concatenate
example. I somewhat dislike that because `object` is not homogeneously
typed and we thus lose type information. This also affects functions
that wish to cast inputs to a common type (ufuncs also do this
sometimes).
A further example of this and discussion is at the end of the mail [1].
So the first question is whether we can form an agreement that an error
is the better choice for `concatenate` and `np.promote_types()`.
I.e. there is no one dtype that can faithfully represent both strings
and integers. (This is currently the case e.g. for datetime64 and
float64.)
The second question is what to do for:
np.array(["string", 0])
which currently always returns strings. Arguably, it must also either
return an `object` array, or raise an error (requiring the user to pick
string or object using `dtype=object`).
The default would be to create a FutureWarning that an `object` array
will be returned for `np.asarray(["string", 0])` in the future.
But if we know already that we prefer an error, it would be better to
give a DeprecationWarning right away. (It just does not seem nice to
change the same thing twice even if the workaround is identical.)
Cheers,
Sebastian
[1]
A second more in-depth point is that code such as:
common_dtype = np.result_type(arr1, arr2) # or promote_types
arr1 = arr1.astype(common_dtype, copy=False)
arr2 = arr2.astype(common_dtype, copy=False)
will currently use `string` in this case while it would error in the
future. This already fails with other type combinations such as
`datetime64` and `float64` at the moment.
The main alternative to this proposal is to return `object` for the
common dtype, since an object array is not homogeneously typed, it
arguably can represent both inputs. I do not quite like this choice
personally because in the above example, it may be that the next line
is something like:
return arr1 * arr2
in which case, the preferred return may be `str` and not `object`.
We currently never promote to `object` unless one of the arrays is
already an `object` array, and that seems like the right choice to me.
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