MyPy doesn't recognize scalar arithmetic for pandas classes

[WillAyd]

Something like:

1 + pd.Series([1])

will yield error: Unsupported operand types for + ("int" and "Series") I assume because our arithmetic ops are added at runtime and not statically. Would be nice to support this somehow though

[jorisvandenbossche]

@WillAyd How can you ever know this "statically" ?
To know if two types can be added up like that, you could need to know if Series.__add__ returns NotImplemented for that type or not, which always seems something run-time dependent?

[WillAyd]

It’s not the definition as much as the declaration. So something like add would have to exist statically for my py to recognize. right now we use factory methods at runtime to add that

[jbrockmendel]

This comes up often enough i wouldn't mind changing our runtime-pinning to something like:

def __add__(self, other)
     return factory_func(self, other, operator.add)

[WillAyd]

I don't mind adding these statically. I think talked with @jreback in the past though who may have disagreed so pinging for his input, otherwise would take a PR

[jreback]

yeah ok with doing more static things as it will help with typing

[jorisvandenbossche]

And apart from typing, it will also result in easier to understand code, I think

[simonjayhawkins]

the last part of #34869 (comment) is related

yeah ok with doing more static things as it will help with typing

+1

And apart from typing, it will also result in easier to understand code, I think

+1

have started down this route on a few commonly used dynamically added methods, to avoid ignores.

pandas/pandas/core/indexes/datetimes.py

Lines 222 to 240 in 6648439

	# --------------------------------------------------------------------
	# methods that dispatch to array and wrap result in DatetimeIndex
	@doc(DatetimeArray.tz_convert)
	def tz_convert(self, tz) -> "DatetimeIndex":
	arr = self._data.tz_convert(tz)
	return type(self)._simple_new(arr, name=self.name)
	@doc(DatetimeArray.tz_localize)
	def tz_localize(
	self, tz, ambiguous="raise", nonexistent="raise"
	) -> "DatetimeIndex":
	arr = self._data.tz_localize(tz, ambiguous, nonexistent)
	return type(self)._simple_new(arr, name=self.name)
	@doc(DatetimeArray.to_period)
	def to_period(self, freq=None) -> "DatetimeIndex":
	arr = self._data.to_period(freq)
	return type(self)._simple_new(arr, name=self.name)

pandas/pandas/core/indexes/period.py

Lines 150 to 158 in a0e0571

	# --------------------------------------------------------------------
	# methods that dispatch to array and wrap result in PeriodIndex
	@doc(PeriodArray.asfreq)
	def asfreq(self, freq=None, how: str = "E") -> "PeriodIndex":
	arr = self._data.asfreq(freq, how)
	return type(self)._simple_new(arr, name=self.name)
	# ------------------------------------------------------------------------

when I did the first one I thought it was clearer to include the actual code for clarity, but after adding a couple more, it does look more like boilerplate and @jbrockmendel solution may be better #31160 (comment) Will look again.