diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index e609b60a0f968..43b491a9716fc 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -164,7 +164,7 @@ def _deserialize_double_vector(ba, offset=0):
     nb = len(ba) - offset
     if nb < 5:
         raise TypeError("_deserialize_double_vector called on a %d-byte array, "
-                "which is too short" % nb)
+                        "which is too short" % nb)
     if ba[offset] == DENSE_VECTOR_MAGIC:
         return _deserialize_dense_vector(ba, offset)
     elif ba[offset] == SPARSE_VECTOR_MAGIC:
@@ -272,6 +272,7 @@ def _serialize_labeled_point(p):
     header_float[0] = p.label
     return header + serialized_features
 
+
 def _deserialize_labeled_point(ba, offset=0):
     """Deserialize a LabeledPoint from a mutually understood format."""
     from pyspark.mllib.regression import LabeledPoint
@@ -283,6 +284,7 @@ def _deserialize_labeled_point(ba, offset=0):
     features = _deserialize_double_vector(ba, offset + 9)
     return LabeledPoint(label, features)
 
+
 def _copyto(array, buffer, offset, shape, dtype):
     """
     Copy the contents of a vector to a destination bytearray at the
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index db39ed0acdb66..71f4ad1a8d44e 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -247,6 +247,7 @@ def stringify(vector):
         else:
             return "[" + ",".join([str(v) for v in vector]) + "]"
 
+
 def _test():
     import doctest
     (failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS)
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index e24c144f458bd..a707a9dcd5b49 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -24,7 +24,6 @@
 from pyspark.serializers import NoOpSerializer
 
 
-
 class MLUtils:
     """
     Helper methods to load, save and pre-process data used in MLlib.
@@ -154,7 +153,6 @@ def saveAsLibSVMFile(data, dir):
         lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
         lines.saveAsTextFile(dir)
 
-
     @staticmethod
     def loadLabeledPoints(sc, path, minPartitions=None):
         """