API: More permissive conversion to StringDtype

[topper-123]

• closes convert numeric column to dedicated pd.StringDtype() #31204
• tests added / passed
• passes black pandas
• passes git diff upstream/master -u -- "*.py" | flake8 --diff
• whatsnew entry

This is a proposal to make using StringDtype more permissive and be usable inplace of dtype=str.

ATM converting to StringDtype will only accept arrays that are str already, meaning you will often have to use astype(str).astype("string") to be sure not to get errors, which can be tedious. For example these fail in master and work in this PR:

>>> pd.Series([1,2, np.nan], dtype="string")
0       1
1       2
2    <NA>
dtype: string
>>> pd.array([1,2, np.nan], dtype="string")
<StringArray>
['1', '2', <NA>]
Length: 3, dtype: string
>>> pd.Series([1,2, np.nan]).astype("string")
0     1.0
1     2.0
2    <NA>
dtype: string
>>> pd.Series([1,2, np.nan], dtype="Int64").astype("string")
0       1
1       2
2    <NA>
dtype: string

etc. now work. Previously the above all gave errors.

Obviously tests and doc updates are still missing, but I would appreciate feedback if this solution looks ok.

[jorisvandenbossche]

@topper-123 is there a reason you closed this?

[topper-123]

I had only tested against a subset of the tests, and it didn't pass all tests when I pushed to upstream.

In general, I've found that type conversions in Pandas are quite complex, so I'm still working on getting something to work that is more than a temporary solution.