QST: Why is subtracting pandas.timedelta from pandas.date not vectorized? about pandas HOT 7 OPEN

df["day"] = df["t1"].dt.date

^ this returns an dtype=object column contain Python datetime.date objects. dtype=object type columns are loosely typed and not vectorizable

You would either have to live with df["day"] being a datetime instead of a date, or alternately use pyarrow types for a stricter differentiation between date / datetime.

I'm not 100% clear on what your SO question is trying to accomplish since the timedelta you are constructing measures nanosecond differences, but I think this is what you are after:

import pandas as pd
import pyarrow as pa

s1 = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
s1 = pd.to_datetime(s1)

df = pd.DataFrame(s1)
df = df.rename(columns={0: "t1", 1: "t2"})
df["day"] = df["t1"].astype(pd.ArrowDtype(pa.date32()))
df["n"] = pd.to_timedelta(df["t1"].dt.day_of_week)

df["week"] = df["day"] - df["n"]

@jbrockmendel for any other guidance

from pandas.

id suggest using a Period[D] dtype. I'd be open to making obj.dt.date do that.

from pandas.

That makes sense from purely a pandas perspective. I think the downside is when you start talking about I/O (thinking especially with databases where DATE / TIMESTAMP are usually distinct types) I'm not sure how proper our Period support would be. With tools like ADBC the arrow types are already accounted for.

Not going to solve that issue in this issue per se - just food for larger thought

from pandas.

I'm pretty sure you've mentioned concerns like that before. how difficult would it be to make Period[D] work like you expect with a database? is that concern a show-stopper for many users?

from pandas.

Not sure. To be honest I don't know a ton of the internals on that - I'm sure its possible but I just question if its worth the effort when its already been done by pyarrow.

FWIW using dtype_backend="pyarrow" with read_csv will return dates as date32 already, so that would be something else we'd have to wire into the parsers. date32 is also exclusively a date type; I suppose a period could represent more things that we would have to handle when serializing outwards (ex: Period("D") may make sense for a DATE database type, but what about Period("Q")?)

from pandas.

when its already been done by pyarrow.

IIUC the suggestion you are implicitly making (here and in #58220) is to have obj.dt.date return with date32[pyarrow] dtype. The trouble with this is 1) pyarrow is not required and 2) it would give users mixed-and-matched null-propagation semantics, which we agreed we needed to avoid when implementing the hybrid string dtype. So for the foreseeable future i just don't see that as a viable option. Period[D] is our de facto date dtype (there has been discussion of making a DateDtype as a thin wrapper around this, but im not finding it on the tracker).

FWIW converting a Period[D] PeriodArray to date32[pyarrow] can be done with:

i4vals = arr.view("i8").astype("int32")
dt32 = pa.array(i4vals, type="date32")

(assuming the astype to int32 doesn't overflow)

from pandas.

Right now I think ser.dt.date should only return a pa.date32 if the series is a pa.timestamp. I agree I don't want to mix those systems, so I see your point about that returning a period when the call is a datetime64.

Im +/- 0 on that versus encouraging more arrow date / timestamp usage

from pandas.