BUG: Fix for convert_dtypes with mix of int and string

[Dr-Irv]

• closes convert_dtypes fails with int and str #32117
• tests added / passed
  • added new cases for test_convert_dtypes
• passes black pandas
• passes git diff upstream/master -u -- "*.py" | flake8 --diff
• whatsnew entry
  • placed in v1.0.2 whatsnew

[jorisvandenbossche]

Thanks! Small question

[jorisvandenbossche]

It's a bit hard to interpret below tests / diff, but does pd.Series([1, 2.0], dtype=object).convert_dtypes() still give Int64?

[Dr-Irv]

Yes. Here's the interpretation of the tests for that case:
The code reads:

            (
                [1, 2.0],
                object,
                {
                    ((True,), (True, False), (True,), (True, False)): "Int64",
                    ((True,), (True, False), (False,), (True, False)): np.dtype(
                        "float"
                    ),
                    ((False,), (True, False), (True, False), (True, False)): np.dtype(
                        "object"
                    ),
                },
            ),

This means the following:

1. Create a Series with [1, 2.0] as the entries, with dtype object
2. Consider the 16 possible combinations of the 4 arguments infer_objects, convert_string, convert_integer and convert_boolean
3. If infer_objects==True and convert_integer==True, result should be Int64
4. If infer_objects==True and convert_integer==False, result should be float
5. If infer_objects==False, result is always object

Prior to this PR, the tests were as follows:
p3) If convert_integer==True, result should be Int64 independent of value of infer_objects
p4) If infer_objects==True and convert_integer==False, result should be float (same)
p5) If infer_objects==False and convert_integer==False, result is object

I think the new version is what we want the behavior to be, i.e., if you start with object and you don't do the infer-objects step, it remains an object.

[jorisvandenbossche]

OK, thanks

[jorisvandenbossche]

@Dr-Irv Thanks!