Pandas adding new row to frame breaks types

xref https://github.com/pydata/pandas/issues/8596

>>> df
   a
0  1
>>> df.dtypes
a    int64
dtype: object
>>> df.loc[1, 'a'] = 2
>>> df
   a
0  1
1  2
>>> df.dtypes
a    float64
dtype: object

in an intermediate step nan is inserted, and the data are casted to floats

Comment From: jreback

type conversion is not done here. as it can be expensive. imagine doing this repeatedly (not recommended anyhow).

This is a side-effect of this type of enlargement. a user can use concat if they want type preservation. However, you can fix the impl to avoid the 2-step creation with missing then filling.

Is that the intention here? as an enhnacement?

Comment From: behzadnouri

agreed that a code change to handle this should not be expensive in terms of performance cost; added the issue just because https://github.com/pydata/pandas/issues/8596 is a similar issue, so there may be a solution which works for both cases.

Comment From: goretkin

can concat guarantee that the new row is added in-place?

Comment From: shoyer

can concat guarantee that the new row is added in-place?

concat never updates in-place.

Comment From: jorisvandenbossche

This actually seems a duplicate of https://github.com/pydata/pandas/issues/6485 (BUG/ENH: dtype change on enlargement).