I'm trying to adjust a date by adding a specified number of business days but I would like to adjust for weekends. The weekend days, however, could change depending on the record. So if my data set looks like this: ┌────────────┬────────┬──────────┬──────────┐ │ DT ┆ N_DAYS ┆ WKND1 ┆ WKND2 │ │ --- ┆ --- ┆ --- ┆ --- │ │ date ┆ i64 ┆ str ┆ str │ ╞════════════╪════════╪══════════╪══════════╡ │ 2025-01-02 ┆ 2 ┆ Saturday ┆ Sunday │ │ 2025-01-09 ┆ 2 ┆ Friday ┆ Saturday │ │ 2025-01-10 ┆ 2 ┆ Saturday ┆ null │ │ 2025-01-15 ┆ 1 ┆ Saturday ┆ Sunday │ └────────────┴────────┴──────────┴──────────┘  I can apply: df = df.with_columns(pl.col('DT').dt.add_business_days(pl.col('N_DAYS')).alias('NEW_DT')) ┌────────────┬────────┬──────────┬──────────┬────────────┐ │ DT ┆ N_DAYS ┆ WKND1 ┆ WKND2 ┆ NEW_DT │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ date ┆ i64 ┆ str ┆ str ┆ date │ ╞════════════╪════════╪══════════╪══════════╪════════════╡ │ 2025-01-02 ┆ 2 ┆ Saturday ┆ Sunday ┆ 2025-01-06 │ │ 2025-01-09 ┆ 2 ┆ Friday ┆ Saturday ┆ 2025-01-13 │ │ 2025-01-10 ┆ 2 ┆ Saturday ┆ null ┆ 2025-01-14 │ │ 2025-01-15 ┆ 1 ┆ Saturday ┆ Sunday ┆ 2025-01-16 │ └────────────┴────────┴──────────┴──────────┴────────────┘  However, I've been trying to generate a week_mask tuple for each of the records based on columns WKND1, WKND2 and apply it as part of my transformation so for the first record, the tuple should be: (True, True, True, True, True, False, False)  Second Record would be: (True, True, True, True, False, False, True)  and so on. Based on the example below the actual response should be: ┌────────────┬────────┬──────────┬──────────┬────────────┐ │ DT ┆ N_DAYS ┆ WKND1 ┆ WKND2 ┆ NEW_DT │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ date ┆ i64 ┆ str ┆ str ┆ date │ ╞════════════╪════════╪══════════╪══════════╪════════════╡ │ 2025-01-02 ┆ 2 ┆ Saturday ┆ Sunday ┆ 2025-01-06 │ │ 2025-01-09 ┆ 2 ┆ Friday ┆ Saturday ┆ 2025-01-14 │ │ 2025-01-10 ┆ 2 ┆ Saturday ┆ null ┆ 2025-01-13 │ │ 2025-01-15 ┆ 1 ┆ Saturday ┆ Sunday ┆ 2025-01-16 │ └────────────┴────────┴──────────┴──────────┴────────────┘  How can I generate the tuple based on column values and apply it dynamically? I tried creating a new column containing a list and using something like this: df = df.with_columns(pl.col('DT').dt.add_business_days(pl.col('N_DAYS'), week_mask=pl.col('W_MASK')).alias('NEW_DT'))  but getting: TypeError: argument 'week_mask': 'Expr' object cannot be converted to 'Sequence' 
week_mask supposed to be be Iterable, so it seems you can't pass expression there. You can iterate over different masks though:  pl.DataFrame.partition_by() to split DataFrame into dict of dataframes. process dataframes, creating week_mask out of partition key. pl.concat() to concat result dataframes together.  weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] pl.concat([ v.with_columns( pl.col('DT').dt.add_business_days( pl.col('N_DAYS'), week_mask=[x not in k for x in weekdays] ).alias('NEW_DT') ) for k, v in df.partition_by('WKND1','WKND2', as_dict = True).items() ]).sort('DT')  shape: (4, 5) ┌────────────┬────────┬──────────┬──────────┬────────────┐ │ DT ┆ N_DAYS ┆ WKND1 ┆ WKND2 ┆ NEW_DT │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ date ┆ i64 ┆ str ┆ str ┆ date │ ╞════════════╪════════╪══════════╪══════════╪════════════╡ │ 2025-01-02 ┆ 2 ┆ Saturday ┆ Sunday ┆ 2025-01-06 │ │ 2025-01-09 ┆ 2 ┆ Friday ┆ Saturday ┆ 2025-01-13 │ │ 2025-01-10 ┆ 2 ┆ Saturday ┆ null ┆ 2025-01-13 │ │ 2025-01-15 ┆ 1 ┆ Saturday ┆ Sunday ┆ 2025-01-16 │ └────────────┴────────┴──────────┴──────────┴────────────┘ 