Closed
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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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