diff --git a/crates/polars-plan/src/plans/conversion/dsl_to_ir.rs b/crates/polars-plan/src/plans/conversion/dsl_to_ir.rs
index 2f74f10237f9..60512a9b0703 100644
--- a/crates/polars-plan/src/plans/conversion/dsl_to_ir.rs
+++ b/crates/polars-plan/src/plans/conversion/dsl_to_ir.rs
@@ -681,8 +681,20 @@ pub fn to_alp_impl(lp: DslPlan, ctxt: &mut DslConversionContext) -> PolarsResult
 
             let subset = options
                 .subset
-                .map(|s| expand_selectors(s, input_schema.as_ref(), &[]))
+                .map(|s| {
+                    let cols = expand_selectors(s, input_schema.as_ref(), &[])?;
+
+                    // Checking if subset columns exist in the dataframe
+                    for col in cols.iter() {
+                        let _ = input_schema
+                            .try_get(col)
+                            .map_err(|_| polars_err!(col_not_found = col))?;
+                    }
+
+                    Ok::<_, PolarsError>(cols)
+                })
                 .transpose()?;
+
             let options = DistinctOptionsIR {
                 subset,
                 maintain_order: options.maintain_order,
diff --git a/py-polars/tests/unit/operations/unique/test_unique.py b/py-polars/tests/unit/operations/unique/test_unique.py
index 800b1dde396a..406a70b6e71f 100644
--- a/py-polars/tests/unit/operations/unique/test_unique.py
+++ b/py-polars/tests/unit/operations/unique/test_unique.py
@@ -2,12 +2,17 @@
 
 import re
 from datetime import date
+from typing import TYPE_CHECKING, Any
 
 import pytest
 
 import polars as pl
+from polars.exceptions import ColumnNotFoundError
 from polars.testing import assert_frame_equal, assert_series_equal
 
+if TYPE_CHECKING:
+    from polars._typing import PolarsDataType
+
 
 def test_unique_predicate_pd() -> None:
     lf = pl.LazyFrame(
@@ -163,6 +168,34 @@ def test_unique_with_null() -> None:
     assert_frame_equal(df.unique(maintain_order=True), expected_df)
 
 
+@pytest.mark.parametrize(
+    ("input_json_data", "input_schema", "subset"),
+    [
+        ({"ID": [], "Name": []}, {"ID": pl.Int64, "Name": pl.String}, "id"),
+        ({"ID": [], "Name": []}, {"ID": pl.Int64, "Name": pl.String}, ["age", "place"]),
+        (
+            {"ID": [1, 2, 1, 2], "Name": ["foo", "bar", "baz", "baa"]},
+            {"ID": pl.Int64, "Name": pl.String},
+            "id",
+        ),
+        (
+            {"ID": [1, 2, 1, 2], "Name": ["foo", "bar", "baz", "baa"]},
+            {"ID": pl.Int64, "Name": pl.String},
+            ["age", "place"],
+        ),
+    ],
+)
+def test_unique_with_bad_subset(
+    input_json_data: dict[str, list[Any]],
+    input_schema: dict[str, PolarsDataType],
+    subset: str | list[str],
+) -> None:
+    df = pl.DataFrame(input_json_data, schema=input_schema)
+
+    with pytest.raises(ColumnNotFoundError, match="not found"):
+        df.unique(subset=subset)
+
+
 def test_categorical_unique_19409() -> None:
     df = pl.DataFrame({"x": [str(n % 50) for n in range(127)]}).cast(pl.Categorical)
     uniq = df.unique()