numpy · AnkitAhlawat7742 · Jul 14, 2025 · Jul 14, 2025 · Jul 14, 2025 · Jul 16, 2025
diff --git a/numpy/lib/_arraysetops_impl.py b/numpy/lib/_arraysetops_impl.py
@@ -21,6 +21,7 @@
 import numpy as np
 from numpy._core import overrides
 from numpy._core._multiarray_umath import _array_converter, _unique_hash
+from numpy.lib.array_utils import normalize_axis_index
 
 array_function_dispatch = functools.partial(
     overrides.array_function_dispatch, module='numpy')
@@ -248,14 +249,17 @@ def unique(ar, return_index=False, return_inverse=False,
     >>> a = np.array([[1, 1], [2, 3]])
     >>> np.unique(a)
     array([1, 2, 3])
-
-    Return the unique rows of a 2D array
+    >>> a = np.array([np.nan,0,0,np.nan])
+    >>> u = np.unique(a, axis=0, equal_nan=True)
+    >>> u
+    array([ 0., nan])
+    # Return the unique rows of a 2D array
 
     >>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
     >>> np.unique(a, axis=0)
     array([[1, 0, 0], [2, 3, 4]])
 
-    Return the indices of the original array that give the unique values:
+    # Return the indices of the original array that give the unique values:
 
     >>> a = np.array(['a', 'b', 'b', 'c', 'a'])
     >>> u, indices = np.unique(a, return_index=True)
@@ -290,7 +294,9 @@ def unique(ar, return_index=False, return_inverse=False,
 
     """
     ar = np.asanyarray(ar)
-    if axis is None:
+    if axis is None or ar.ndim == 1:
+        if axis is not None:
+            normalize_axis_index(axis, ar.ndim)
         ret = _unique1d(ar, return_index, return_inverse, return_counts,
                         equal_nan=equal_nan, inverse_shape=ar.shape, axis=None,
                         sorted=sorted)

diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
@@ -1256,3 +1256,21 @@ def test_unique_with_matrix(self, data, transpose, dtype):
         u = np.unique(mat)
         expected = np.unique(np.asarray(mat))
         assert_array_equal(u, expected, strict=True)
+
+    def test_unique_axis0_equal_nan_on_1d_array(self):
+        # Test Issue #29336
+        arr1d = np.array([np.nan, 0, 0, np.nan])
+        expected = np.array([0., np.nan])
+        result = np.unique(arr1d, axis=0, equal_nan=True)
+        assert_array_equal(result, expected)
+
+    def test_unique_axis_minus1_eq_on_1d_array(self):
+        arr1d = np.array([np.nan, 0, 0, np.nan])
+        expected = np.array([0., np.nan])
+        result = np.unique(arr1d, axis=-1, equal_nan=True)
+        assert_array_equal(result, expected)
+
+    def test_unique_axis_float_raises_typeerror(self):
+        arr1d = np.array([np.nan, 0, 0, np.nan])
+        with pytest.raises(TypeError, match="integer argument expected"):
+            np.unique(arr1d, axis=0.0, equal_nan=False)