BUG: pandas series creation fails with OverflowError when given large integers

[mdering]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample, a copy-pastable example

>>> import pandas as pd
>>> pd.Series(1000000000000000000000)
0    1000000000000000000000
dtype: object
>>> pd.Series(1000000000000000000000, index = pd.date_range(pd.Timestamp.now().floor("1D"), pd.Timestamp.now(), freq='T'))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py", line 327, in __init__
    data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
  File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/construction.py", line 475, in sanitize_array
    subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)
  File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py", line 1555, in construct_1d_arraylike_from_scalar
    subarr.fill(value)
OverflowError: int too big to convert
>>> pd.Series(1000000000000000000000.0, index = pd.date_range(pd.Timestamp.now().floor("1D"), pd.Timestamp.now(), freq='T'))
2020-09-11 00:00:00    1.000000e+21
2020-09-11 00:01:00    1.000000e+21
2020-09-11 00:02:00    1.000000e+21
2020-09-11 00:03:00    1.000000e+21
2020-09-11 00:04:00    1.000000e+21
                           ...
2020-09-11 11:24:00    1.000000e+21
2020-09-11 11:25:00    1.000000e+21
2020-09-11 11:26:00    1.000000e+21
2020-09-11 11:27:00    1.000000e+21
2020-09-11 11:28:00    1.000000e+21
Freq: T, Length: 689, dtype: float64

Problem description

Hi pandas, when creating a new series with very large integers, series creation fails. This is not true if you pass in a float, or if you just pass in one value of a series and no index. the traceback points to pandas code so I'm submitting a bug here.

Expected Output

I would expect this to fail more gracefully or either output a series of object type, or float type. when initializing an array in numpy, it transparently converts it to object type

>>> np.array([1000000000000000000000]*1000).dtype
dtype('O')

Output of pd.show_versions()

INSTALLED VERSIONS

commit : f2ca0a2
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : 1.7.0
feather : None
xlsxwriter : 1.3.3
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1

[arw2019]

Confirming that this happens on 1.2 master

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : 03c7040 python : 3.8.3.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-47-generic Version : #51-Ubuntu SMP Fri Sep 4 19:50:52 UTC 2020 machine : x86_64 processor : byteorder : little LC_ALL : C.UTF-8 LANG : C.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.2.0.dev0+318.g03c704087
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0.post20200704
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.19.0
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : 0.6.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1