How to write the resulting RDD to a csv file in Spark python

I have a resulting RDD labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions). This has output in this format:

[(0.0, 0.08482142857142858), (0.0, 0.11442786069651742),.....]

What I want is to create a CSV file with one column for labels (the first part of the tuple in above output) and one for predictions(second part of tuple output). But I don't know how to write to a CSV file in Spark using Python.

How can I create a CSV file with the above output?

Just map the lines of the RDD (labelsAndPredictions) into strings (the lines of the CSV) then use rdd.saveAsTextFile().

def toCSVLine(data):
  return ','.join(str(d) for d in data)

lines = labelsAndPredictions.map(toCSVLine)
lines.saveAsTextFile('hdfs://my-node:9000/tmp/labels-and-predictions.csv')

I know this is an old post. But to help someone searching for the same, here's how I write a two column RDD to a single CSV file in PySpark 1.6.2

The RDD:

>>> rdd.take(5)
[(73342, u'cells'), (62861, u'cell'), (61714, u'studies'), (61377, u'aim'), (60168, u'clinical')]

Now the code:

# First I convert the RDD to dataframe
from pyspark import SparkContext
df = sqlContext.createDataFrame(rdd, ['count', 'word'])

The DF:

>>> df.show()
+-----+-----------+
|count|       word|
+-----+-----------+
|73342|      cells|
|62861|       cell|
|61714|    studies|
|61377|        aim|
|60168|   clinical|
|59275|          2|
|59221|          1|
|58274|       data|
|58087|development|
|56579|     cancer|
|50243|    disease|
|49817|   provided|
|49216|   specific|
|48857|     health|
|48536|      study|
|47827|    project|
|45573|description|
|45455|  applicant|
|44739|    program|
|44522|   patients|
+-----+-----------+
only showing top 20 rows

Now write to CSV

# Write CSV (I have HDFS storage)
df.coalesce(1).write.format('com.databricks.spark.csv').options(header='true').save('file:///home/username/csv_out')

P.S: I am just a beginner learning from posts here in Stackoverflow. So I don't know whether this is the best way. But it worked for me and I hope it will help someone!