BUG: uint8 silently converted to int8 during dataframe creation

• 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 master branch of pandas.

Reproducible Example

# !pip install -U pandas
import pandas as pd
import numpy as np

# Create arbitrary 3-channel 8-bit image
data = np.random.randint(0, 256, size=(50,50, 3), dtype='uint8')

# Replicate this image and treat each image as a row of pixel features
rows = [data.reshape(-1)]*2
assert all(r.dtype == np.uint8 for r in rows)

print(pd.DataFrame(rows).dtypes)
# int8???

print(pd.DataFrame(np.vstack(rows)).dtypes)
# uint8 (expected)

Issue Description

Apologies if this issue already exists – type-related issues are hard for me to navigate in github since there are too many to easily parse.

uint8 subarray dtypes are silently converted to int8 when constructing a dataframe from lists.

I tried testing on master but Windows has a build error for me (stack trace below),

building 'pandas._libs.algos' extension
  error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
  ----------------------------------------
  ERROR: Failed building wheel for pandas
Failed to build pandas
ERROR: Could not build wheels for pandas which use PEP 517 and cannot be installed directly

Expected Behavior

The dtype of each unit in each column is a uint8, so I would expect uint8 resulting columns

Installed Versions

INSTALLED VERSIONS

commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.4.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252

pandas : 1.3.3 numpy : 1.19.5 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.3 setuptools : 52.0.0.post20210125 Cython : None pytest : 6.2.4 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.4.3 lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.23.1 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : None odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.3 sqlalchemy : 1.4.15 tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : None

[ntjess]

@mroeschke I have updated to only rely on numpy and pandas per your request. The issue still persists on my end.

[mroeschke]

I think this was solved by https://github.com/pandas-dev/pandas/pull/47475 which will be apart of the 1.5 release, so closing