BUG: unexpected strange behaviour with pandas and numpy compatibility

[moroclash]

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

arr = [np.array([[196]], dtype=np.uint8)] * 100
arr = [i.flat[0] for i in arr]

print(arr[:5])
col = pd.DataFrame(data={"c1": arr })

print(col.head())

Issue Description

this produces a list of-60, actually, any number less than 127 will still be as is, but above that, it changes to a negative number, so any number will be in that range 1 to 127, and if larger than 127 it becomes within that range -1 to 127.

Expected Behavior

this should produce a list of 195s but that has not happened.

Installed Versions

INSTALLED VERSIONS

commit : 66e3805
python : 3.7.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-89-generic
Version : #100-Ubuntu SMP Fri Sep 24 14:50:10 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.5
numpy : 1.21.5
pytz : 2019.1
dateutil : 2.8.0
pip : 22.0.3
setuptools : 59.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.32.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fsspec : 2022.02.0
fastparquet : None
gcsfs : 2022.02.0
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 7.0.0
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.4.31
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.55.1

[jreback]

uint8 max range is 128 and this is the default behavior to overflow

you have to be extremely careful with small dtypes like this