How to find 'not null' data in polars

import polars as pl

data = {
    'd1': [20, 31, 56, 44, 10, None],
    'd2': [37, 15, 27, 36, None, None],
    'd3': [48, 4, None, 88, None, None],
    'd4': [50, None, None, 9, None, None],
}
data_df = pl.DataFrame(data)
print(data_df)

result = {
    'd1': [20, 31, 56, 44, 10, None],
    'd2': [37, 15, 27, 36, None, None],
    'd3': [48, 4, None, 88, None, None],
    'd4': [50, None, None, 9, None, None],
    'result': [50, 4, 27, 9, 10, None]
}
result_df = pl.DataFrame(result)
print(result_df)

How to find the rightmost 'not null' data in each row.

As in the example, turn data_df into result_df.

Polars version = 0.17.12

There is pl.coalesce to get the first non-null.

You can pass the reversed columns to get the last.

df.with_columns(result = pl.coalesce(reversed(df.columns)))

shape: (6, 5)
┌──────┬──────┬──────┬──────┬────────┐
│ d1   ┆ d2   ┆ d3   ┆ d4   ┆ result │
│ ---  ┆ ---  ┆ ---  ┆ ---  ┆ ---    │
│ i64  ┆ i64  ┆ i64  ┆ i64  ┆ i64    │
╞══════╪══════╪══════╪══════╪════════╡
│ 20   ┆ 37   ┆ 48   ┆ 50   ┆ 50     │
│ 31   ┆ 15   ┆ 4    ┆ null ┆ 4      │
│ 56   ┆ 27   ┆ null ┆ null ┆ 27     │
│ 44   ┆ 36   ┆ 88   ┆ 9    ┆ 9      │
│ 10   ┆ null ┆ null ┆ null ┆ 10     │
│ null ┆ null ┆ null ┆ null ┆ null   │
└──────┴──────┴──────┴──────┴────────┘

You can implement it "manually" by creating a list and dropping the nulls.

df.with_columns(result = 
   pl.concat_list(pl.all())
     .arr.eval(pl.element().drop_nulls())
     .arr.last()
)

Perhaps another option is to use pl.fold

df.with_columns(result = 
   pl.fold(
      acc=None, 
      exprs=pl.all(), 
      function=lambda left, right: right.fill_null(left)
   )
)