[SOLVED] DST temporal feature from timestamp using polars

DST temporal feature from timestamp using polars

I'm migrating code to polars from pandas. I have time-series data consisting of a timestamp and value column and I need to compute a bunch of features. i.e.

from datetime import datetime, timedelta

df = pl.DataFrame({
    "timestamp": pl.datetime_range(
        datetime(2017, 1, 1),
        datetime(2018, 1, 1),
        timedelta(minutes=15),
        time_zone="Australia/Sydney",
        time_unit="ms", eager=True),
        })
        
value = np.random.normal(0, 1, len(df))
df = df.with_columns([pl.Series(value).alias("value")])

I need to generate a column containing an indicator if the timestamp is standard or daylight time. I'm currently using map_elements because as far as I can see the isn't a Temporal Expr, i.e. my current code is

def dst(timestamp:datetime):
    return int(timestamp.dst().total_seconds()!=0)

df = df.with_columns(pl.struct("timestamp").map_elements(lambda x: dst(**x)).alias("dst"))

(this uses a trick that effectively checks if the tzinfo.dst(dt) offset is zero or not)

Is there a (fast) way of doing this using polars expressions rather than (slow) map_elements?

Solution

With polars>=0.18.5 the following works

df = df.with_columns((pl.col("timestamp").dt.dst_offset()==0).cast(pl.Int32).alias("dst"))