How to add a new column in a Polars dataframe where the value is based on different conditions

I have a dataframe that contains a number of fields and I would like to add an additional column as a label based on these fields. Ideally the code would look something like this:

df.with_columns(
    [
        pl.when(~pl.all(pl.col("temp").is_null()) & pl.all(pl.col("daily_temp").is_null())).then("sensor_1_" + pl.col("location")).alias("label"),
        pl.when(pl.all(pl.col("temp").is_null()) & ~pl.all(pl.col("daily_temp").is_null())).then("sensor_2_" + pl.col("loc")).alias("label"),
    ]
)

The conditions are irrelevant, however I would like to be able to use multiple conditions as described above to add a value to the same column. Using the code as described will throw a duplicate column error, is there a way to do this properly?

As is mentioned in the comments, you can chain then when/then/when/then.

It should look like this:

df.with_columns(
        (pl.when(~pl.all(pl.col("temp").is_null()) & pl.all(pl.col("daily_temp").is_null()))
          .then("sensor_1_" + pl.col("location"))
          .when(pl.all(pl.col("temp").is_null()) & ~pl.all(pl.col("daily_temp").is_null()))
          .then("sensor_2_" + pl.col("loc"))).alias("label")
)

I'm not sure why you'd want to do it this way but you could use coalesce instead of chaining when/then

That would look like:

df.with_columns(
    pl.coalesce(
    [
        pl.when(~pl.all(pl.col("temp").is_null()) & pl.all(pl.col("daily_temp").is_null())).then("sensor_1_" + pl.col("location")),
        pl.when(pl.all(pl.col("temp").is_null()) & ~pl.all(pl.col("daily_temp").is_null())).then("sensor_2_" + pl.col("loc")),
    ]
    ).alias("label")
)

coalesce looks for the first not-null thing that is passed to it which accomplishes the same thing as chaining. I'm not sure if the chained conditional is optimized and more performant.