BUG: Fixed bug when creating new column with missing values when setting a single string value

[MarcoGorelli]

• closes BUG: Creating a string column on a mask results in NaN being stringyfied and potentially truncated based on the 'maxchar' value of the column #56204 (Replace xxxx with the GitHub issue number)
• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

[phofl]

Can you put this into the bug column?

[jbrockmendel]

is it important that this be a lambda?

[jbrockmendel]

maybe make this one test and parametrize? in 3.0 we'll only want to end up with one test here right?

[MarcoGorelli]

thanks for your reviews! have updated

[jbrockmendel]

Just tracked down an old branch where past-me was trying to get rid of infer_fill_value. The only recoverable bit of that branch looks like it would go just before L1880 in indexing.py

                        elif not is_list_like(value):
                            # Find our empty_value dtype by constructing an array
                            #  from our value and doing a .take on it
                            arr = sanitize_array(value, Index(range(1)), copy=False)
                            taker = -1 * np.ones(len(self.obj), dtype=np.intp)
                            empty_value = algos.take_nd(arr, taker)
                            self.obj[key] = empty_value

would that solve the issue here robustly? i like past-me's idea of using sanitize_array rather than yet-another constructor-esque function

[MarcoGorelli]

That would also avoid the warning when expanding column-wise:

main:

In [1]: df = pd.DataFrame({'a': [.1, .2], 'b': [.3, .4]})

In [2]: df.loc[1, 'c'] = True
<ipython-input-2-d3d953ae007a>:1: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'True' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df.loc[1, 'c'] = True

In [3]: df
Out[3]: 
     a    b     c
0  0.1  0.3   NaN
1  0.2  0.4  True

In [4]: df.dtypes
Out[4]: 
a    float64
b    float64
c     object
dtype: object

here:

In [1]: df = pd.DataFrame({'a': [.1, .2], 'b': [.3, .4]})

In [2]: df.loc[1, 'c'] = True

In [3]: df
Out[3]: 
     a    b     c
0  0.1  0.3   NaN
1  0.2  0.4  True

In [4]: df.dtypes
Out[4]: 
a    float64
b    float64
c     object
dtype: object

[jbrockmendel]

LGTM

[mroeschke]

Could you move this 2.2.0.rst?

[MarcoGorelli]

thanks for your reviews! merging then