From f674f75227267fcc26b7b7f1108e3cf330a605a8 Mon Sep 17 00:00:00 2001
From: Gaetan Semet <gaetan@xeberon.net>
Date: Thu, 1 Sep 2016 14:41:33 +0200
Subject: [PATCH] use map comprehension

Signed-off-by: Gaetan Semet <gaetan@xeberon.net>
---
 examples/src/main/python/ml/quantile_discretizer_example.py | 2 +-
 examples/src/main/python/ml/vector_slicer_example.py        | 4 ++--
 examples/src/main/python/sql/hive.py                        | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/src/main/python/ml/quantile_discretizer_example.py b/examples/src/main/python/ml/quantile_discretizer_example.py
index 788a0baffebb..0fc1d1949a77 100644
--- a/examples/src/main/python/ml/quantile_discretizer_example.py
+++ b/examples/src/main/python/ml/quantile_discretizer_example.py
@@ -29,7 +29,7 @@
         .getOrCreate()
 
     # $example on$
-    data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
+    data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2)]
     df = spark.createDataFrame(data, ["id", "hour"])
     # $example off$
 
diff --git a/examples/src/main/python/ml/vector_slicer_example.py b/examples/src/main/python/ml/vector_slicer_example.py
index d2f46b190f9a..68c8cfe27e37 100644
--- a/examples/src/main/python/ml/vector_slicer_example.py
+++ b/examples/src/main/python/ml/vector_slicer_example.py
@@ -32,8 +32,8 @@
 
     # $example on$
     df = spark.createDataFrame([
-        Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3}),),
-        Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]),)])
+        Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3})),
+        Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]))])
 
     slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1])
 
diff --git a/examples/src/main/python/sql/hive.py b/examples/src/main/python/sql/hive.py
index 9b2a2c4e6a16..98b48908b5a1 100644
--- a/examples/src/main/python/sql/hive.py
+++ b/examples/src/main/python/sql/hive.py
@@ -79,7 +79,7 @@
 
     # You can also use DataFrames to create temporary views within a SparkSession.
     Record = Row("key", "value")
-    recordsDF = spark.createDataFrame(map(lambda i: Record(i, "val_" + str(i)), range(1, 101)))
+    recordsDF = spark.createDataFrame([Record(i, "val_" + str(i)) for i in range(1, 101)])
     recordsDF.createOrReplaceTempView("records")
 
     # Queries can then join DataFrame data with data stored in Hive.