Polars selectors alias with when/then/otherwise

Question

Say I have this:

df = polars.DataFrame(dict(
  j=numpy.random.randint(10, 99, 10),
  k=numpy.random.randint(10, 99, 10),
  l=numpy.random.randint(10, 99, 10),
  ))
  
print(df)

shape: (10, 4)
 j (i64)  k (i64)  l (i64)
 32       82       34
 67       40       53
 11       81       86
 10       13       36
 70       80       62
 91       31       90
 18       59       51
 98       67       92
 23       13       25
 57       78       74
shape: (10, 3)

and I want to apply the same when/then/otherwise condition on multiple columns:

dfj = (df
  .select(
    polars
      .when(polars.selectors.numeric() < 50)
      .then(polars.lit(1))
      .otherwise(polars.lit(2))
    )
  )

This fails with:

polars.exceptions.DuplicateError: the name: 'literal' is duplicate

How do I make this use the currently selected column as the alias? I.e. I want the equivalent of this:

dfj = (df
  .select(
    polars
      .when(polars.col(c) < 50)
      .then(polars.lit(1))
      .otherwise(polars.lit(2))
      .alias(c)
    for c in df.columns
    )
  )

print(dfj)

 j (i32)  k (i32)  l (i32)
 1        2        1
 2        1        2
 1        2        2
 1        1        1
 2        2        2
 2        1        2
 1        2        2
 2        2        2
 1        1        1
 2        2        2
shape: (10, 3)

Answer 1

When using literal values in then/otherwise you can use .name.keep() to take the "original" column names.

(df
  .select(
    polars
      .when(polars.selectors.numeric() < 50)
      .then(polars.lit(1))
      .otherwise(polars.lit(2))
      .name.keep()
    )
  )

shape: (10, 3)
┌─────┬─────┬─────┐
│ j   ┆ k   ┆ l   │
│ --- ┆ --- ┆ --- │
│ i32 ┆ i32 ┆ i32 │
╞═════╪═════╪═════╡
│ 1   ┆ 1   ┆ 2   │
│ 2   ┆ 1   ┆ 1   │
│ 1   ┆ 2   ┆ 1   │
│ 2   ┆ 2   ┆ 1   │
│ 2   ┆ 1   ┆ 2   │
│ 2   ┆ 2   ┆ 2   │
│ 2   ┆ 2   ┆ 1   │
│ 2   ┆ 2   ┆ 2   │
│ 1   ┆ 1   ┆ 2   │
│ 2   ┆ 2   ┆ 1   │
└─────┴─────┴─────┘