diff --git a/python/pyspark/sql/session.py b/python/pyspark/sql/session.py
index f248afa3d839d..ee04c94cbd5d8 100644
--- a/python/pyspark/sql/session.py
+++ b/python/pyspark/sql/session.py
@@ -1139,7 +1139,12 @@ def createDataFrame(  # type: ignore[misc]
             require_minimum_pandas_version()
             if data.ndim not in [1, 2]:
                 raise ValueError("NumPy array input should be of 1 or 2 dimensions.")
-            column_names = ["value"] if data.ndim == 1 else ["_1", "_2"]
+
+            if data.ndim == 1 or data.shape[1] == 1:
+                column_names = ["value"]
+            else:
+                column_names = ["_%s" % i for i in range(1, data.shape[1] + 1)]
+
             if schema is None and not self._jconf.arrowPySparkEnabled():
                 # Construct `schema` from `np.dtype` of the input NumPy array
                 # TODO: Apply the logic below when self._jconf.arrowPySparkEnabled() is True
diff --git a/python/pyspark/sql/tests/test_arrow.py b/python/pyspark/sql/tests/test_arrow.py
index fdba431726c83..6083f31ac81b9 100644
--- a/python/pyspark/sql/tests/test_arrow.py
+++ b/python/pyspark/sql/tests/test_arrow.py
@@ -188,8 +188,10 @@ def create_np_arrs(self):
         return (
             [np.array([1, 2]).astype(t) for t in int_dtypes]
             + [np.array([0.1, 0.2]).astype(t) for t in float_dtypes]
-            + [np.array([[1, 2], [3, 4]]).astype(t) for t in int_dtypes]
-            + [np.array([[0.1, 0.2], [0.3, 0.4]]).astype(t) for t in float_dtypes]
+            + [np.array([[1], [2]]).astype(t) for t in int_dtypes]
+            + [np.array([[0.1], [0.2]]).astype(t) for t in float_dtypes]
+            + [np.array([[1, 1, 1], [2, 2, 2]]).astype(t) for t in int_dtypes]
+            + [np.array([[0.1, 0.1, 0.1], [0.2, 0.2, 0.2]]).astype(t) for t in float_dtypes]
         )
 
     def test_toPandas_fallback_enabled(self):
@@ -510,9 +512,11 @@ def test_schema_conversion_roundtrip(self):
 
     def test_createDataFrame_with_ndarray(self):
         dtypes = ["tinyint", "smallint", "int", "bigint", "float", "double"]
-        expected_dtypes = [[("value", t)] for t in dtypes] + [
-            [("_1", t), ("_2", t)] for t in dtypes
-        ]
+        expected_dtypes = (
+            [[("value", t)] for t in dtypes]
+            + [[("value", t)] for t in dtypes]
+            + [[("_1", t), ("_2", t), ("_3", t)] for t in dtypes]
+        )
         arrs = self.create_np_arrs
 
         for arr, dtypes in zip(arrs, expected_dtypes):