diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
index 5f7d5696b71a..aa545e1a0c00 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
@@ -183,11 +183,19 @@ class UnivocityParser(
     }
   }
 
+  private val doParse = if (schema.nonEmpty) {
+    (input: String) => convert(tokenizer.parseLine(input))
+  } else {
+    // If `columnPruning` enabled and partition attributes scanned only,
+    // `schema` gets empty.
+    (_: String) => InternalRow.empty
+  }
+
   /**
    * Parses a single CSV string and turns it into either one resulting row or no row (if the
    * the record is malformed).
    */
-  def parse(input: String): InternalRow = convert(tokenizer.parseLine(input))
+  def parse(input: String): InternalRow = doParse(input)
 
   private def convert(tokens: Array[String]): InternalRow = {
     if (tokens.length != schema.length) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
index d2f166c7d187..d7a99a022185 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
@@ -1602,4 +1602,14 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils with Te
     assert(testAppender2.events.asScala
       .exists(msg => msg.getRenderedMessage.contains("CSV header does not conform to the schema")))
   }
+
+  test("SPARK-24645 skip parsing when columnPruning enabled and partitions scanned only") {
+    withSQLConf(SQLConf.CSV_PARSER_COLUMN_PRUNING.key -> "true") {
+      withTempPath { path =>
+        val dir = path.getAbsolutePath
+        spark.range(10).selectExpr("id % 2 AS p", "id").write.partitionBy("p").csv(dir)
+        checkAnswer(spark.read.csv(dir).selectExpr("sum(p)"), Row(5))
+      }
+    }
+  }
 }