Fix PEP8 violations in examples/src/main/python.

Author: Reynold Xin <rxin@apache.org>

Closes #870 from rxin/examples-python-pep8 and squashes the following commits:

2829e84 [Reynold Xin] Fix PEP8 violations in examples/src/main/python.

6 files changed, 25 insertions, 19 deletions

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index f0b46cd28b..1a7c4c51f4 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -29,22 +29,25 @@ from pyspark import SparkContext
 LAMBDA = 0.01   # regularization
 np.random.seed(42)
 
+
 def rmse(R, ms, us):
     diff = R - ms * us.T
     return np.sqrt(np.sum(np.power(diff, 2)) / M * U)
 
+
 def update(i, vec, mat, ratings):
     uu = mat.shape[0]
     ff = mat.shape[1]
-    
+
     XtX = mat.T * mat
     Xty = mat.T * ratings[i, :].T
-    
+
     for j in range(ff):
-        XtX[j,j] += LAMBDA * uu
-    
+        XtX[j, j] += LAMBDA * uu
+
     return np.linalg.solve(XtX, Xty)
 
+
 if __name__ == "__main__":
     """
     Usage: als [M] [U] [F] [iterations] [slices]"
@@ -57,10 +60,10 @@ if __name__ == "__main__":
     slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
     print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
-            (M, U, F, ITERATIONS, slices)
+        (M, U, F, ITERATIONS, slices)
 
     R = matrix(rand(M, F)) * matrix(rand(U, F).T)
-    ms = matrix(rand(M ,F))
+    ms = matrix(rand(M, F))
     us = matrix(rand(U, F))
 
     Rb = sc.broadcast(R)
@@ -71,8 +74,9 @@ if __name__ == "__main__":
         ms = sc.parallelize(range(M), slices) \
                .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
                .collect()
-        ms = matrix(np.array(ms)[:, :, 0])      # collect() returns a list, so array ends up being
-                                                # a 3-d array, we take the first 2 dims for the matrix
+        # collect() returns a list, so array ends up being
+        # a 3-d array, we take the first 2 dims for the matrix
+        ms = matrix(np.array(ms)[:, :, 0])
         msb = sc.broadcast(ms)
 
         us = sc.parallelize(range(U), slices) \
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py
index fc16586c28..988fc45baf 100755
--- a/examples/src/main/python/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -59,7 +59,7 @@ if __name__ == "__main__":
 
     while tempDist > convergeDist:
         closest = data.map(
-            lambda p : (closestPoint(p, kPoints), (p, 1)))
+            lambda p: (closestPoint(p, kPoints), (p, 1)))
         pointStats = closest.reduceByKey(
             lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2))
         newPoints = pointStats.map(
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py
index 0f22d0b323..6c33deabfd 100755
--- a/examples/src/main/python/logistic_regression.py
+++ b/examples/src/main/python/logistic_regression.py
@@ -60,8 +60,8 @@ if __name__ == "__main__":
 
     # Compute logistic regression gradient for a matrix of data points
     def gradient(matrix, w):
-        Y = matrix[:,0]    # point labels (first column of input file)
-        X = matrix[:,1:]   # point coordinates
+        Y = matrix[:, 0]    # point labels (first column of input file)
+        X = matrix[:, 1:]   # point coordinates
         # For each point (x, y), compute gradient function, then sum these up
         return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1)
 
diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py
index d350fa46fa..0b96343158 100755
--- a/examples/src/main/python/pagerank.py
+++ b/examples/src/main/python/pagerank.py
@@ -15,9 +15,8 @@
 # limitations under the License.
 #
 
-#!/usr/bin/env python
-
-import re, sys
+import re
+import sys
 from operator import add
 
 from pyspark import SparkContext
@@ -26,7 +25,8 @@ from pyspark import SparkContext
 def computeContribs(urls, rank):
     """Calculates URL contributions to the rank of other URLs."""
     num_urls = len(urls)
-    for url in urls: yield (url, rank / num_urls)
+    for url in urls:
+        yield (url, rank / num_urls)
 
 
 def parseNeighbors(urls):
@@ -59,8 +59,8 @@ if __name__ == "__main__":
     # Calculates and updates URL ranks continuously using PageRank algorithm.
     for iteration in xrange(int(sys.argv[2])):
         # Calculates URL contributions to the rank of other URLs.
-        contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
-            computeContribs(urls, rank))
+        contribs = links.join(ranks).flatMap(
+            lambda (url, (urls, rank)): computeContribs(urls, rank))
 
         # Re-calculates URL ranks based on neighbor contributions.
         ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py
index 234720b55f..21d94a2cd4 100755
--- a/examples/src/main/python/pi.py
+++ b/examples/src/main/python/pi.py
@@ -29,9 +29,11 @@ if __name__ == "__main__":
     sc = SparkContext(appName="PythonPi")
     slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     n = 100000 * slices
+
     def f(_):
         x = random() * 2 - 1
         y = random() * 2 - 1
         return 1 if x ** 2 + y ** 2 < 1 else 0
+
     count = sc.parallelize(xrange(1, n+1), slices).map(f).reduce(add)
     print "Pi is roughly %f" % (4.0 * count / n)
diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py
index 4913ee926a..41d00c1b79 100755
--- a/examples/src/main/python/sort.py
+++ b/examples/src/main/python/sort.py
@@ -27,8 +27,8 @@ if __name__ == "__main__":
     sc = SparkContext(appName="PythonSort")
     lines = sc.textFile(sys.argv[1], 1)
     sortedCount = lines.flatMap(lambda x: x.split(' ')) \
-                  .map(lambda x: (int(x), 1)) \
-                  .sortByKey(lambda x: x)
+        .map(lambda x: (int(x), 1)) \
+        .sortByKey(lambda x: x)
     # This is just a demo on how to bring all the sorted data back to a single node.
     # In reality, we wouldn't want to collect all the data to the driver node.
     output = sortedCount.collect()