[Python] How to Filter All Columns in a Polars DataFrame by expression?

I have this example Polars DataFrame:

import polars as pl

df = pl.DataFrame({
"id": [1, 2, 3, 4, 5],
"variable1": [15, None, 5, 10, 20],
"variable2": [40, 30, 50, 10, None],
})


I'm trying to filter all columns of my dataframe using the method pl.all(), and I also tried using pl.any_horizontal() == Condition. However I'm getting the following error:

ComputeError: The predicate passed to 'LazyFrame.filter' expanded to multiple expressions:

col("id").is_not_null(),
col("variable1").is_not_null(),
col("variable2").is_not_null(),
This is ambiguous. Try to combine the predicates with the 'all' or `any' expression.


Here are my attemps to try to face this.

# Attempt 1:
(
df
.filter(
pl.all().is_not_null()
)
)
# Attempt 2:
(
df
.filter(
pl.any_horizontal().is_not_null()
)
)


Desired output, but it's not scalable for bigger DataFrames:

(
df
.filter(
pl.col("variable1").is_not_null(),
pl.col("variable2").is_not_null()
)
)


How can I filter all columns in a scalable way without specifying each column individually?

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