Overflow in to_datetime when using nanoseconds

[DieterDePaepe]

Code Sample

nano_time = '1518071940360000000'
print(pd.to_datetime(pd.to_numeric(nano_time), unit="ns")) # Works
print(pd.to_datetime(nano_time, unit="ns")) # OverflowError: Python int too large to convert to C long

Problem description

It appears the to_numeric function is not suited for very long numbers in string form, which is a problem for parsing nano-second based times. The method should be smarter in processing its input.

Expected Output

Not an error.

Timestamp('2018-02-08 06:39:00.360000')

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.23.0
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[jreback]

the error message is actually incorrect. passing a unit requires an integer (and not a string).

this is almost a duplicate of #15836

cc @chris-b1

@DieterDePaepe if you wanted to submit a PR to fix would be great.

[jreback]

note canonically you can simply do

In [9]: pd.to_datetime(int(nano_time))
Out[9]: Timestamp('2018-02-08 06:39:00.360000')