BUG: assignment with enlargement gives object dtype with ExtensionArrays

[jorisvandenbossche]

From #32271, when setting with enlargment, the dtype gets converted into object dtype (while for normal integer or boolean dtype this is not the case):

In [10]: s = pd.Series([1, 2, 3], dtype="Int64")  

In [11]: s[3] = 4 

In [12]: s   
Out[12]: 
0    1
1    2
2    3
3    4
dtype: object

In [13]: s = pd.Series([1, 2, 3], dtype="int64") 

In [14]: s[3] = 4   

In [15]: s  
Out[15]: 
0    1
1    2
2    3
3    4
dtype: int64

It also happens with eg DecimalArray from our tests, so suppose it is a general issue with ExtensionArrays.

[phofl]

This works now. Returns:

0    1
1    2
2    3
3    4
dtype: Int64

[kasim95]

take

[kasim95]

@phofl I found the same issue still occurring for other dtypes apart from Int64.
I used the following code:

In [1]: dtype = "Int32"
        result = Series([1, 2, 3], dtype=dtype)
        previous_dtype = result.dtype
        result.loc[3] = 4
        print(f"{previous_dtype} -> {result.dtype}")

Out [1]:Int32 -> Int64

My observations for some of the inconsistent dtype conversions are as follows:

Before Assignment	After Assignment
Int32	Int64
Int16	Int64
Int8	Int64
UInt64	Float64
UInt32	Int64
UInt16	Int64
UInt8	Int64
Float64	object
Float32	object
Float16 (float16)	float64
string	object

Also, these inconsistent conversion only occur for assignments to indexes that do not exist in the original array.
For example, in the above code, index 3 does not exist in the result array.
Is this behavior deliberate or is it a bug?