BUG: uint16 inserted as int16 when assigning row with dict

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np

df = pd.DataFrame(columns=["actual", "reference"])
df.loc[0] = {'actual': np.uint16(40_000), 'reference': "nope"}
df

#    actual reference
# 0  -25536      nope

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 2 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   actual     1 non-null      int16 
 1   reference  1 non-null      object
dtypes: int16(1), object(1)

Issue Description

Inserting a row with a dict, uint16 values are converted to int16 and the value conversion does not preserve the correct value. This also happens when assigning into an existing object-typed column (the conversion sequence seems to be -> int16 -> int in that case).

Expected Behavior

It’s expected the dtype is preserved - uint16 if possible, or an int which is large enough to represent the value.

Installed Versions

python           : 3.8.10.final.0
python-bits      : 64
OS               : Linux
machine          : x86_64

pandas           : 1.4.2
numpy            : 1.22.4
pytz             : 2022.1
dateutil         : 2.8.2
pip              : 20.0.2
setuptools       : 44.0.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.1.2
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
brotli           : None
fastparquet      : None
fsspec           : None
gcsfs            : None
markupsafe       : 2.1.1
matplotlib       : None
numba            : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyreadstat       : None
pyxlsb           : None
s3fs             : None
scipy            : None
snappy           : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
zstandard        : None

[simonjayhawkins]

note working in pandas-1.2.5, albeit retaining object dtype from empty DataFrame and then in pandas-1.3.5 overflow but still with object dtype

first bad commit: [549e39bf7a4f925424b39e613895594f48dbb1a5] ENH: Make maybe_convert_object respect dtype itemsize (#40908) cc @rhshadrach

and then in pandas-1.4.2/main overflow but changed (incorrect) dtype.

I suspect as result of https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.4.0.html#ignoring-dtypes-in-concat-with-empty-or-all-na-columns and that addressing the above regression resolves the dtype issue

[simonjayhawkins]

Thanks @bluss for the report

note working in pandas-1.2.5, albeit retaining object dtype from empty DataFrame and then in pandas-1.3.5 overflow but still with object dtype and then in pandas-1.4.2/main overflow but changed (incorrect) dtype.

print(pd.__version__)
df = pd.DataFrame(columns=["actual", "reference"])
df.loc[0] = {"actual": np.uint16(40_000), "reference": "nope"}
print(df)
print(df.dtypes)

# 1.2.5
#   actual reference
# 0  40000      nope
# actual       object
# reference    object
# dtype: object

# 1.3.5
#    actual reference
# 0  -25536      nope
# actual       object
# reference    object
# dtype: object

# 1.4.2
#    actual reference
# 0  -25536      nope
# actual        int16
# reference    object
# dtype: object