how to apply fill_null to a given set of columns on a lazyframe

How do I fill nulls only in determined columns? Is there a way of implementing a subset?

Right now I am doing this:

df.withcolumns(pl.col("col1").fill_null(strategy='zero'),
               pl.col("col2").fill_null(strategy="zero"))

I think there should be a way to do something like

df.fill_null(strategy='zero', subset=['col1', 'col2'])

pl.col is more versatile, it not just takes in one column, but multiple columns and regex and wildcards. For more check out the documentation of pl.col .

So your problem becomes straight forward

import polars as pl

df = pl.DataFrame(
    {
        "a": [8, 9, 10, 11, None, None],
        "b": [None, 4, 4, 4, None, 4],
    }
)

df.with_columns(pl.col("a", "b").fill_null(strategy="zero"))



shape: (6, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 8   ┆ 0   │
│ 9   ┆ 4   │
│ 10  ┆ 4   │
│ 11  ┆ 4   │
│ 0   ┆ 0   │
│ 0   ┆ 4   │
└─────┴─────┘