From e4a9504f8d96955b39e1eb7ef0d7de7c8fbd1a5f Mon Sep 17 00:00:00 2001
From: Raashish Aggarwal <94279692+raashish1601@users.noreply.github.com>
Date: Wed, 7 Oct 2026 22:24:34 +0530
Subject: [PATCH 1/2] fix: support non-float polars columns in is_nan_or_none

---
 tests/test_processing.py    | 11 +++++++++++
 utilsforecast/processing.py |  4 ++++
 2 files changed, 15 insertions(+)

diff --git a/tests/test_processing.py b/tests/test_processing.py
index fbe94ab..3cc22a2 100644
--- a/tests/test_processing.py
+++ b/tests/test_processing.py
@@ -117,6 +117,17 @@ def test_is_nan_or_none():
             is_nan_or_none(pl.Series([np.nan, 1.0, None])).to_numpy(),
             np.array([True, False, True]),
         )
+        for values in (["a", None, "b"], [True, None, False], [1, None, 2]):
+            np.testing.assert_equal(
+                is_nan_or_none(pl.Series(values)).to_numpy(),
+                np.array([False, True, False]),
+            )
+        np.testing.assert_equal(
+            is_nan_or_none(
+                pl.Series(["a", None, "b"], dtype=pl.Categorical)
+            ).to_numpy(),
+            np.array([False, True, False]),
+        )
 
 
 def test_vertical_concat_pd():
diff --git a/utilsforecast/processing.py b/utilsforecast/processing.py
index c6bf34d..75b38ba 100644
--- a/utilsforecast/processing.py
+++ b/utilsforecast/processing.py
@@ -247,6 +247,10 @@ def is_none(s: Series) -> Series:
 
 
 def is_nan_or_none(s: Series) -> Series:
+    if isinstance(s, pl_Series) and not s.dtype.is_float():
+        # polars only supports is_nan on numeric columns and only floats
+        # can hold NaN, so other columns (e.g. strings) can only have nulls
+        return s.is_null()
     return is_nan(s) | is_none(s)
 
 

From a4c1d452a2e878ef3ca6899f7da0d5550f8026d7 Mon Sep 17 00:00:00 2001
From: Raashish Aggarwal <94279692+raashish1601@users.noreply.github.com>
Date: Sat, 10 Oct 2026 10:33:14 +0530
Subject: [PATCH 2/2] move the non-float polars handling to is_nan

---
 tests/test_processing.py    | 25 ++++++++++++++-----------
 utilsforecast/processing.py |  9 ++++-----
 2 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/tests/test_processing.py b/tests/test_processing.py
index 3cc22a2..77b2aaa 100644
--- a/tests/test_processing.py
+++ b/tests/test_processing.py
@@ -93,6 +93,20 @@ def test_is_nan():
         is_nan(pl.Series([np.nan, 1.0, None])).to_numpy(),
         np.array([True, False, None]),
     )
+    for series in (
+        pl.Series(["a", None, "b"]),
+        pl.Series([True, None, False]),
+        pl.Series([1, None, 2]),
+        pl.Series(["a", None, "b"], dtype=pl.Categorical),
+    ):
+        np.testing.assert_equal(
+            is_nan(series).to_numpy(),
+            np.array([False, False, False]),
+        )
+        np.testing.assert_equal(
+            is_nan_or_none(series).to_numpy(),
+            np.array([False, True, False]),
+        )
 
 
 def test_is_none():
@@ -117,17 +131,6 @@ def test_is_nan_or_none():
             is_nan_or_none(pl.Series([np.nan, 1.0, None])).to_numpy(),
             np.array([True, False, True]),
         )
-        for values in (["a", None, "b"], [True, None, False], [1, None, 2]):
-            np.testing.assert_equal(
-                is_nan_or_none(pl.Series(values)).to_numpy(),
-                np.array([False, True, False]),
-            )
-        np.testing.assert_equal(
-            is_nan_or_none(
-                pl.Series(["a", None, "b"], dtype=pl.Categorical)
-            ).to_numpy(),
-            np.array([False, True, False]),
-        )
 
 
 def test_vertical_concat_pd():
diff --git a/utilsforecast/processing.py b/utilsforecast/processing.py
index 75b38ba..691fcfd 100644
--- a/utilsforecast/processing.py
+++ b/utilsforecast/processing.py
@@ -233,8 +233,11 @@ def filter_with_mask(
 def is_nan(s: Series) -> Series:
     if isinstance(s, pd.Series):
         out = s.isna()
-    else:
+    elif s.dtype.is_float():
         out = s.is_nan()
+    else:
+        # polars only supports is_nan on numeric columns and only floats can hold NaN
+        out = pl.repeat(False, s.len(), eager=True).alias(s.name)
     return out
 
 
@@ -247,10 +250,6 @@ def is_none(s: Series) -> Series:
 
 
 def is_nan_or_none(s: Series) -> Series:
-    if isinstance(s, pl_Series) and not s.dtype.is_float():
-        # polars only supports is_nan on numeric columns and only floats
-        # can hold NaN, so other columns (e.g. strings) can only have nulls
-        return s.is_null()
     return is_nan(s) | is_none(s)