ENH: Timedelta cannot span more than 293 years => implementation limitation
The Pandas datetime arithmetic module is indeed very useful and it can be used for archeological research studies as well. Unfortunately, the limitation that Timedelta object cannot span more than 293 years puts a huge shame on this wonderful piece of data science library. With this limitation, it is not possible to go back for thousands or even more than 300 years in history.
```
>>> pd.to_timedelta('1Y')*5
Timedelta('1826 days 05:06:00')
>>> pd.to_timedelta('1Y')*292
Timedelta('106650 days 19:26:24')
>>> pd.to_timedelta('1Y')*293
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pandas/_libs/tslibs/timedeltas.pyx", line 1347, in pandas._libs.tslibs.timedeltas.Timedelta.__mul__
  File "pandas/_libs/tslibs/timedeltas.pyx", line 1230, in pandas._libs.tslibs.timedeltas.Timedelta.__new__
  File "pandas/_libs/tslibs/timedeltas.pyx", line 180, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64
  File "pandas/_libs/tslibs/timedeltas.pyx", line 308, in pandas._libs.tslibs.timedeltas.cast_from_unit
OverflowError: Python int too large to convert to C long
```
In terms of practicality, time resolution and time span are always contradicting with each other. Nowadays, quantum physics often deals with time objects at a scale of nano (10**-9), pico (10**-12), or even femto(10**-15)-second. While archeology often deals with time objects at a span of thousands, millions, or even billions of years. If I remember correctly, pandas set the time counter base unit at nano-seconds, thus, the span will be short. The solution to cater for both high resolution and large span is to use floating point rather than a large integer, as the time counter base unit. The speed will be slightly slower for floating point. But if you take a look at Intel Architecture, on modern CPU, floating point arithmetic is almost the same as integer especially when SIMD is used.