Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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(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: float64Problem 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