diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetScanBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetScanBuilder.scala
index c579867623e1..d844240a1a3b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetScanBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetScanBuilder.scala
@@ -130,10 +130,9 @@ case class ParquetScanBuilder(
       // are combined with filter or group by
       // e.g. SELECT COUNT(col1) FROM t WHERE col2 = 8
       //      SELECT COUNT(col1) FROM t GROUP BY col2
-      // Todo: 1. add support if groupby column is partition col
-      //          (https://issues.apache.org/jira/browse/SPARK-36646)
-      //       2. add support if filter col is partition col
-      //          (https://issues.apache.org/jira/browse/SPARK-36647)
+      // However, if the filter is on partition column, max/min/count can still be pushed down
+      // Todo:  add support if groupby column is partition col
+      //        (https://issues.apache.org/jira/browse/SPARK-36646)
       return false
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAggregatePushDownSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAggregatePushDownSuite.scala
index c795bd9ff338..4422a6a6cb7e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAggregatePushDownSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAggregatePushDownSuite.scala
@@ -129,10 +129,9 @@ abstract class ParquetAggregatePushDownSuite
         .write.partitionBy("p").parquet(dir.getCanonicalPath)
       withTempView("tmp") {
         spark.read.parquet(dir.getCanonicalPath).createOrReplaceTempView("tmp");
-        val enableVectorizedReader = Seq("false", "true")
-        for (testVectorizedReader <- enableVectorizedReader) {
+        Seq("false", "true").foreach { enableVectorizedReader =>
           withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
-            vectorizedReaderEnabledKey -> testVectorizedReader) {
+            vectorizedReaderEnabledKey -> enableVectorizedReader) {
             val count = sql("SELECT COUNT(p) FROM tmp")
             count.queryExecution.optimizedPlan.collect {
               case _: DataSourceV2ScanRelation =>
@@ -221,7 +220,7 @@ abstract class ParquetAggregatePushDownSuite
     }
   }
 
-  test("aggregate push down - query with filter not push down") {
+  test("aggregate push down - aggregate with data filter cannot be pushed down") {
     val data = Seq((-2, "abc", 2), (3, "def", 4), (6, "ghi", 2), (0, null, 19),
       (9, "mno", 7), (2, null, 7))
     withParquetTable(data, "t") {
@@ -240,6 +239,29 @@ abstract class ParquetAggregatePushDownSuite
     }
   }
 
+  test("aggregate push down - aggregate with partition filter can be pushed down") {
+    withTempPath { dir =>
+      spark.range(10).selectExpr("id", "id % 3 as p")
+        .write.partitionBy("p").parquet(dir.getCanonicalPath)
+      withTempView("tmp") {
+        spark.read.parquet(dir.getCanonicalPath).createOrReplaceTempView("tmp");
+        Seq("false", "true").foreach { enableVectorizedReader =>
+          withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
+            vectorizedReaderEnabledKey -> enableVectorizedReader) {
+            val max = sql("SELECT max(id), min(id), count(id) FROM tmp WHERE p = 0")
+            max.queryExecution.optimizedPlan.collect {
+              case _: DataSourceV2ScanRelation =>
+                val expected_plan_fragment =
+                  "PushedAggregation: [MAX(id), MIN(id), COUNT(id)]"
+                checkKeywordsExistsInExplain(max, expected_plan_fragment)
+            }
+            checkAnswer(max, Seq(Row(9, 0, 4)))
+          }
+        }
+      }
+    }
+  }
+
   test("aggregate push down - push down only if all the aggregates can be pushed down") {
     val data = Seq((-2, "abc", 2), (3, "def", 4), (6, "ghi", 2), (0, null, 19),
       (9, "mno", 7), (2, null, 7))
@@ -356,10 +378,9 @@ abstract class ParquetAggregatePushDownSuite
       spark.createDataFrame(rdd, schema).write.parquet(file.getCanonicalPath)
       withTempView("test") {
         spark.read.parquet(file.getCanonicalPath).createOrReplaceTempView("test")
-        val enableVectorizedReader = Seq("false", "true")
-        for (testVectorizedReader <- enableVectorizedReader) {
+        Seq("false", "true").foreach { enableVectorizedReader =>
           withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
-            vectorizedReaderEnabledKey -> testVectorizedReader) {
+            vectorizedReaderEnabledKey -> enableVectorizedReader) {
 
             val testMinWithTS = sql("SELECT min(StringCol), min(BooleanCol), min(ByteCol), " +
               "min(BinaryCol), min(ShortCol), min(IntegerCol), min(LongCol), min(FloatCol), " +
@@ -477,10 +498,9 @@ abstract class ParquetAggregatePushDownSuite
   }
 
   test("aggregate push down - column name case sensitivity") {
-    val enableVectorizedReader = Seq("false", "true")
-    for (testVectorizedReader <- enableVectorizedReader) {
+    Seq("false", "true").foreach { enableVectorizedReader =>
       withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
-        vectorizedReaderEnabledKey -> testVectorizedReader) {
+        vectorizedReaderEnabledKey -> enableVectorizedReader) {
         withTempPath { dir =>
           spark.range(10).selectExpr("id", "id % 3 as p")
             .write.partitionBy("p").parquet(dir.getCanonicalPath)