diff --git a/examples/src/main/python/ml/cross_validator.py b/examples/src/main/python/ml/cross_validator.py
index a41df6cf946fb..283db5d953311 100644
--- a/examples/src/main/python/ml/cross_validator.py
+++ b/examples/src/main/python/ml/cross_validator.py
@@ -83,10 +83,10 @@
 
     # Prepare test documents, which are unlabeled.
     test = spark.createDataFrame([
-        (4L, "spark i j k"),
-        (5L, "l m n"),
-        (6L, "mapreduce spark"),
-        (7L, "apache hadoop")
+        (4, "spark i j k"),
+        (5, "l m n"),
+        (6, "mapreduce spark"),
+        (7, "apache hadoop")
     ], ["id", "text"])
 
     # Make predictions on test documents. cvModel uses the best model found (lrModel).
diff --git a/examples/src/main/python/ml/pipeline_example.py b/examples/src/main/python/ml/pipeline_example.py
index bd10cfd7a252b..1926cd26fa6ff 100644
--- a/examples/src/main/python/ml/pipeline_example.py
+++ b/examples/src/main/python/ml/pipeline_example.py
@@ -35,10 +35,10 @@
     # $example on$
     # Prepare training documents from a list of (id, text, label) tuples.
     training = spark.createDataFrame([
-        (0L, "a b c d e spark", 1.0),
-        (1L, "b d", 0.0),
-        (2L, "spark f g h", 1.0),
-        (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
+        (0, "a b c d e spark", 1.0),
+        (1, "b d", 0.0),
+        (2, "spark f g h", 1.0),
+        (3, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
 
     # Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
     tokenizer = Tokenizer(inputCol="text", outputCol="words")
@@ -51,10 +51,10 @@
 
     # Prepare test documents, which are unlabeled (id, text) tuples.
     test = spark.createDataFrame([
-        (4L, "spark i j k"),
-        (5L, "l m n"),
-        (6L, "mapreduce spark"),
-        (7L, "apache hadoop")], ["id", "text"])
+        (4, "spark i j k"),
+        (5, "l m n"),
+        (6, "mapreduce spark"),
+        (7, "apache hadoop")], ["id", "text"])
 
     # Make predictions on test documents and print columns of interest.
     prediction = model.transform(test)
diff --git a/examples/src/main/python/mllib/binary_classification_metrics_example.py b/examples/src/main/python/mllib/binary_classification_metrics_example.py
index daf000e38dcd0..91f8378f29c0c 100644
--- a/examples/src/main/python/mllib/binary_classification_metrics_example.py
+++ b/examples/src/main/python/mllib/binary_classification_metrics_example.py
@@ -39,7 +39,7 @@
         .rdd.map(lambda row: LabeledPoint(row[0], row[1]))
 
     # Split data into training (60%) and test (40%)
-    training, test = data.randomSplit([0.6, 0.4], seed=11L)
+    training, test = data.randomSplit([0.6, 0.4], seed=11)
     training.cache()
 
     # Run training algorithm to build the model
diff --git a/examples/src/main/python/mllib/multi_class_metrics_example.py b/examples/src/main/python/mllib/multi_class_metrics_example.py
index cd56b3c97c778..7dc5fb4f9127f 100644
--- a/examples/src/main/python/mllib/multi_class_metrics_example.py
+++ b/examples/src/main/python/mllib/multi_class_metrics_example.py
@@ -32,7 +32,7 @@
     data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")
 
     # Split data into training (60%) and test (40%)
-    training, test = data.randomSplit([0.6, 0.4], seed=11L)
+    training, test = data.randomSplit([0.6, 0.4], seed=11)
     training.cache()
 
     # Run training algorithm to build the model