Series.quantile returns None with datetime type data

[jeanai28]

Checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of Polars.

Reproducible example

import polars as pl
import datetime as dt

ts = pl.Series([dt.datetime(2023,10,3,3), dt.datetime(2023,11,1,2)])
print(ts.quantile(0.5))

Log output

None

Issue description

quantile function will return None if the input colume or series is datetime type. It works fine if I converted it into Int64 type.

Expected behavior

return a datetime of the same type of the input column

Installed versions

Replace this line with the output of pl.show_versions(). Leave the backticks in place.

--------Version info---------
Polars: 0.19.13
Index type: UInt32
Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.35
Python: 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0]

----Optional dependencies----
adbc_driver_sqlite:
cloudpickle:
connectorx:
deltalake:
fsspec:
gevent:
matplotlib: 3.7.2
numpy: 1.26.0
openpyxl:
pandas: 2.1.1
pyarrow: 14.0.1
pydantic:
pyiceberg:
pyxlsb:
sqlalchemy:
xlsx2csv:
xlsxwriter:

[reswqa]

I'm actually a little unsure whether operation like sum/mean/quantile makes sense for datetime dtype.

[jeanai28]

I agree that sum and mean may not be meaningful for datetimes, but I don't see any ambiguity in quantile/min/max operator since the datetime is well ordered. I find that I can use np.quantile(pl.Series([dt.datetime(2023,10,3,3), dt.datetime(2023,11,1,2)]), 0.5) to work around it.