[SPARK-3361] Expand PEP 8 checks to include EC2 script and Python examples

This PR resolves [SPARK-3361](https://issues.apache.org/jira/browse/SPARK-3361) by expanding the PEP 8 checks to cover the remaining Python code base:
* The EC2 script
* All Python / PySpark examples

Author: Nicholas Chammas <nicholas.chammas@gmail.com>

Closes #2297 from nchammas/pep8-rulez and squashes the following commits:

1e5ac9a [Nicholas Chammas] PEP 8 fixes to Python examples
c3dbeff [Nicholas Chammas] PEP 8 fixes to EC2 script
65ef6e8 [Nicholas Chammas] expand PEP 8 checks

4 files changed, 12 insertions, 10 deletions

diff --git a/examples/src/main/python/mllib/correlations.py b/examples/src/main/python/mllib/correlations.py
index 6b16a56e44..4218eca822 100755
--- a/examples/src/main/python/mllib/correlations.py
+++ b/examples/src/main/python/mllib/correlations.py
@@ -28,7 +28,7 @@ from pyspark.mllib.util import MLUtils
 
 
 if __name__ == "__main__":
-    if len(sys.argv) not in [1,2]:
+    if len(sys.argv) not in [1, 2]:
         print >> sys.stderr, "Usage: correlations (<file>)"
         exit(-1)
     sc = SparkContext(appName="PythonCorrelations")
diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py
index 6e4a4a0cb6..61ea4e06ec 100755
--- a/examples/src/main/python/mllib/decision_tree_runner.py
+++ b/examples/src/main/python/mllib/decision_tree_runner.py
@@ -21,7 +21,9 @@ Decision tree classification and regression using MLlib.
 This example requires NumPy (http://www.numpy.org/).
 """
 
-import numpy, os, sys
+import numpy
+import os
+import sys
 
 from operator import add
 
@@ -127,7 +129,7 @@ if __name__ == "__main__":
     (reindexedData, origToNewLabels) = reindexClassLabels(points)
 
     # Train a classifier.
-    categoricalFeaturesInfo={} # no categorical features
+    categoricalFeaturesInfo = {}  # no categorical features
     model = DecisionTree.trainClassifier(reindexedData, numClasses=2,
                                          categoricalFeaturesInfo=categoricalFeaturesInfo)
     # Print learned tree and stats.
diff --git a/examples/src/main/python/mllib/random_rdd_generation.py b/examples/src/main/python/mllib/random_rdd_generation.py
index b388d8d83f..1e8892741e 100755
--- a/examples/src/main/python/mllib/random_rdd_generation.py
+++ b/examples/src/main/python/mllib/random_rdd_generation.py
@@ -32,8 +32,8 @@ if __name__ == "__main__":
 
     sc = SparkContext(appName="PythonRandomRDDGeneration")
 
-    numExamples = 10000 # number of examples to generate
-    fraction = 0.1 # fraction of data to sample
+    numExamples = 10000  # number of examples to generate
+    fraction = 0.1  # fraction of data to sample
 
     # Example: RandomRDDs.normalRDD
     normalRDD = RandomRDDs.normalRDD(sc, numExamples)
@@ -45,7 +45,7 @@ if __name__ == "__main__":
     print
 
     # Example: RandomRDDs.normalVectorRDD
-    normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
+    normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows=numExamples, numCols=2)
     print 'Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count()
     print '  First 5 samples:'
     for sample in normalVectorRDD.take(5):
diff --git a/examples/src/main/python/mllib/sampled_rdds.py b/examples/src/main/python/mllib/sampled_rdds.py
index ec64a5978c..92af3af5eb 100755
--- a/examples/src/main/python/mllib/sampled_rdds.py
+++ b/examples/src/main/python/mllib/sampled_rdds.py
@@ -36,7 +36,7 @@ if __name__ == "__main__":
 
     sc = SparkContext(appName="PythonSampledRDDs")
 
-    fraction = 0.1 # fraction of data to sample
+    fraction = 0.1  # fraction of data to sample
 
     examples = MLUtils.loadLibSVMFile(sc, datapath)
     numExamples = examples.count()
@@ -49,9 +49,9 @@ if __name__ == "__main__":
     expectedSampleSize = int(numExamples * fraction)
     print 'Sampling RDD using fraction %g.  Expected sample size = %d.' \
         % (fraction, expectedSampleSize)
-    sampledRDD = examples.sample(withReplacement = True, fraction = fraction)
+    sampledRDD = examples.sample(withReplacement=True, fraction=fraction)
     print '  RDD.sample(): sample has %d examples' % sampledRDD.count()
-    sampledArray = examples.takeSample(withReplacement = True, num = expectedSampleSize)
+    sampledArray = examples.takeSample(withReplacement=True, num=expectedSampleSize)
     print '  RDD.takeSample(): sample has %d examples' % len(sampledArray)
 
     print
@@ -66,7 +66,7 @@ if __name__ == "__main__":
     fractions = {}
     for k in keyCountsA.keys():
         fractions[k] = fraction
-    sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement = True, fractions = fractions)
+    sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement=True, fractions=fractions)
     keyCountsB = sampledByKeyRDD.countByKey()
     sizeB = sum(keyCountsB.values())
     print '  Sampled %d examples using approximate stratified sampling (by label). ==> Sample' \