diff --git a/google/cloud/bigquery/_pandas_helpers.py b/google/cloud/bigquery/_pandas_helpers.py
index ab58b1729..29139ae09 100644
--- a/google/cloud/bigquery/_pandas_helpers.py
+++ b/google/cloud/bigquery/_pandas_helpers.py
@@ -844,7 +844,13 @@ def dataframe_to_json_generator(dataframe):
         output = {}
         for column, value in zip(dataframe.columns, row):
             # Omit NaN values.
-            if pandas.isna(value):
+            is_nan = pandas.isna(value)
+
+            # isna() can also return an array-like of bools, but the latter's boolean
+            # value is ambiguous, hence an extra check. An array-like value is *not*
+            # considered a NaN, however.
+            if isinstance(is_nan, bool) and is_nan:
                 continue
             output[column] = value
+
         yield output
diff --git a/tests/unit/test__pandas_helpers.py b/tests/unit/test__pandas_helpers.py
index a9b0ae21f..f0975ef65 100644
--- a/tests/unit/test__pandas_helpers.py
+++ b/tests/unit/test__pandas_helpers.py
@@ -821,6 +821,41 @@ def test_dataframe_to_json_generator(module_under_test):
     assert list(rows) == expected
 
 
+def test_dataframe_to_json_generator_repeated_field(module_under_test):
+    pytest.importorskip(
+        "pandas",
+        minversion=str(PANDAS_MINIUM_VERSION),
+        reason=(
+            f"Requires `pandas version >= {PANDAS_MINIUM_VERSION}` "
+            "which introduces pandas.NA"
+        ),
+    )
+
+    df_data = [
+        collections.OrderedDict(
+            [("repeated_col", [pandas.NA, 2, None, 4]), ("not_repeated_col", "first")]
+        ),
+        collections.OrderedDict(
+            [
+                ("repeated_col", ["a", "b", mock.sentinel.foo, "d"]),
+                ("not_repeated_col", "second"),
+            ]
+        ),
+    ]
+    dataframe = pandas.DataFrame(df_data)
+
+    rows = module_under_test.dataframe_to_json_generator(dataframe)
+
+    expected = [
+        {"repeated_col": [pandas.NA, 2, None, 4], "not_repeated_col": "first"},
+        {
+            "repeated_col": ["a", "b", mock.sentinel.foo, "d"],
+            "not_repeated_col": "second",
+        },
+    ]
+    assert list(rows) == expected
+
+
 @pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
 def test_list_columns_and_indexes_with_named_index(module_under_test):
     df_data = collections.OrderedDict(
@@ -882,7 +917,7 @@ def test_list_columns_and_indexes_with_multiindex(module_under_test):
 def test_dataframe_to_bq_schema_dict_sequence(module_under_test):
     df_data = collections.OrderedDict(
         [
-            ("str_column", [u"hello", u"world"]),
+            ("str_column", ["hello", "world"]),
             ("int_column", [42, 8]),
             ("bool_column", [True, False]),
         ]
@@ -1070,7 +1105,7 @@ def test_dataframe_to_arrow_dict_sequence_schema(module_under_test):
     ]
 
     dataframe = pandas.DataFrame(
-        {"field01": [u"hello", u"world"], "field02": [True, False]}
+        {"field01": ["hello", "world"], "field02": [True, False]}
     )
 
     arrow_table = module_under_test.dataframe_to_arrow(dataframe, dict_schema)
@@ -1139,8 +1174,8 @@ def test_dataframe_to_parquet_compression_method(module_under_test):
 def test_dataframe_to_bq_schema_fallback_needed_wo_pyarrow(module_under_test):
     dataframe = pandas.DataFrame(
         data=[
-            {"id": 10, "status": u"FOO", "execution_date": datetime.date(2019, 5, 10)},
-            {"id": 20, "status": u"BAR", "created_at": datetime.date(2018, 9, 12)},
+            {"id": 10, "status": "FOO", "execution_date": datetime.date(2019, 5, 10)},
+            {"id": 20, "status": "BAR", "created_at": datetime.date(2018, 9, 12)},
         ]
     )
 
@@ -1167,8 +1202,8 @@ def test_dataframe_to_bq_schema_fallback_needed_wo_pyarrow(module_under_test):
 def test_dataframe_to_bq_schema_fallback_needed_w_pyarrow(module_under_test):
     dataframe = pandas.DataFrame(
         data=[
-            {"id": 10, "status": u"FOO", "created_at": datetime.date(2019, 5, 10)},
-            {"id": 20, "status": u"BAR", "created_at": datetime.date(2018, 9, 12)},
+            {"id": 10, "status": "FOO", "created_at": datetime.date(2019, 5, 10)},
+            {"id": 20, "status": "BAR", "created_at": datetime.date(2018, 9, 12)},
         ]
     )
 
@@ -1197,8 +1232,8 @@ def test_dataframe_to_bq_schema_fallback_needed_w_pyarrow(module_under_test):
 def test_dataframe_to_bq_schema_pyarrow_fallback_fails(module_under_test):
     dataframe = pandas.DataFrame(
         data=[
-            {"struct_field": {"one": 2}, "status": u"FOO"},
-            {"struct_field": {"two": u"222"}, "status": u"BAR"},
+            {"struct_field": {"one": 2}, "status": "FOO"},
+            {"struct_field": {"two": "222"}, "status": "BAR"},
         ]
     )
 
@@ -1252,7 +1287,7 @@ def test_augment_schema_type_detection_succeeds(module_under_test):
                 "timestamp_field": datetime.datetime(2005, 5, 31, 14, 25, 55),
                 "date_field": datetime.date(2005, 5, 31),
                 "bytes_field": b"some bytes",
-                "string_field": u"some characters",
+                "string_field": "some characters",
                 "numeric_field": decimal.Decimal("123.456"),
                 "bignumeric_field": decimal.Decimal("{d38}.{d38}".format(d38="9" * 38)),
             }
@@ -1312,13 +1347,13 @@ def test_augment_schema_type_detection_fails(module_under_test):
     dataframe = pandas.DataFrame(
         data=[
             {
-                "status": u"FOO",
+                "status": "FOO",
                 "struct_field": {"one": 1},
-                "struct_field_2": {"foo": u"123"},
+                "struct_field_2": {"foo": "123"},
             },
             {
-                "status": u"BAR",
-                "struct_field": {"two": u"111"},
+                "status": "BAR",
+                "struct_field": {"two": "111"},
                 "struct_field_2": {"bar": 27},
             },
         ]
@@ -1351,7 +1386,7 @@ def test_dataframe_to_parquet_dict_sequence_schema(module_under_test):
     ]
 
     dataframe = pandas.DataFrame(
-        {"field01": [u"hello", u"world"], "field02": [True, False]}
+        {"field01": ["hello", "world"], "field02": [True, False]}
     )
 
     write_table_patch = mock.patch.object(