BUG: Series(range(...)) fails when range contains values not supported by int64

[jschendel]

Code Sample, a copy-pastable example if possible

The following fails on master:

In [1]: import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1255.gb9f26e21e'

In [2]: pd.Series(range(2**63, 2**63 + 4))
---------------------------------------------------------------------------
OverflowError: Python int too large to convert to C long

In [3]: pd.Series(range(2**73, 2**73 + 4))
---------------------------------------------------------------------------
OverflowError: Python int too large to convert to C long

But is successful when doing an intermediate conversion to list:

In [4]: pd.Series(list(range(2**63, 2**63 + 4)))
Out[4]: 
0    9223372036854775808
1    9223372036854775809
2    9223372036854775810
3    9223372036854775811
dtype: uint64

In [5]: pd.Series(list(range(2**73, 2**73 + 4)))
Out[5]: 
0    9444732965739290427392
1    9444732965739290427393
2    9444732965739290427394
3    9444732965739290427395
dtype: object

Problem description

Series(range(...)) fails when values in the range are not supported by int64, but construction is successful for an equivalent list.

Expected Output

I'd expect In [2]/In [3] to be successful and produce output equivalent to Out[4]/Out[5].

Output of pd.show_versions()

INSTALLED VERSIONS

commit : b9f26e2
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.26.0.dev0+1255.gb9f26e21e
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
pytest : 4.6.2
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8

[topper-123]

This is indeed a bit of an edge case.

Could #30171 be accepted and this issue be expanded to cover dataframes also? This issue and the problem after #30171 is merged are exactly the same, as in both cases pandas does a simple np.arange(..., dtype="int64") in order to convert a range to an array, where (to match the list behaviour), it needs to be a bit more sofisticated.

Probably there should be a range_to_array function, that should be common for data frames and series constructors.