BUG: DataFrame.convert_dtype(convert_integer=True) with int64_t limits throws Warning

[Tishj]

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 pyarrow as pa

print(pd.__version__)
print(pa.__version__)

df = pd.DataFrame({
	'a': [-9223372036854775808, 4611686018427387904, 9223372036854775807, None],
	'b': [-9223372036854775808, 4611686018427387904, 9223372036854775807, None],
	'c': [-9223372036854775808, 4611686018427387904, 9223372036854775807, None],
})

pa_df = df.convert_dtypes(dtype_backend='pyarrow')
print(pa_df)

2.2.2
16.0.0
/Users/User/.pyenv/versions/3.12.1/lib/python3.12/site-packages/pandas/core/dtypes/cast.py:1057: RuntimeWarning: invalid value encountered in cast
  if (arr.astype(int) == arr).all():
/Users/User/.pyenv/versions/3.12.1/lib/python3.12/site-packages/pandas/core/dtypes/cast.py:1081: RuntimeWarning: invalid value encountered in cast
  if (arr.astype(int) == arr).all():
                      a                     b                     c
0  -9223372036854775808  -9223372036854775808  -9223372036854775808
1   4611686018427387904   4611686018427387904   4611686018427387904
2   9223372036854775807   9223372036854775807   9223372036854775807
3                  <NA>                  <NA>                  <NA>

Issue Description

Throwing a warning when converting

Expected Behavior

It should not throw a warning, it looks like the conversion happens successfully in spite of the warning.

Installed Versions

commit : d9cdd2e python : 3.12.1.final.0 python-bits : 64 OS : Darwin OS-release : 23.1.0 Version : Darwin Kernel Version 23.1.0: Mon Oct 9 21:28:45 PDT 2023; root:xnu-10002.41.9~6/RELEASE_ARM64_T6020 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.5.1
pip : 23.2.1
Cython : None
pytest : 8.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.3.1
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 16.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.29
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[mroeschke]

Thanks for the report. Just noting that this happens with any of the backends