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+---
+layout: global
+title: Python Programming Guide
+---
+
+
+The Spark Python API (PySpark) exposes most of the Spark features available in the Scala version to Python.
+To learn the basics of Spark, we recommend reading through the
+[Scala programming guide](scala-programming-guide.html) first; it should be
+easy to follow even if you don't know Scala.
+This guide will show how to use the Spark features described there in Python.
+
+# Key Differences in the Python API
+
+There are a few key differences between the Python and Scala APIs:
+
+* Python is dynamically typed, so RDDs can hold objects of different types.
+* PySpark does not currently support the following Spark features:
+    - Special functions on RDDs of doubles, such as `mean` and `stdev`
+    - `lookup`
+    - `persist` at storage levels other than `MEMORY_ONLY`
+    - `sample`
+    - `sort`
+
+In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types.
+Short functions can be passed to RDD methods using Python's [`lambda`](http://www.diveintopython.net/power_of_introspection/lambda_functions.html) syntax:
+
+{% highlight python %}
+logData = sc.textFile(logFile).cache()
+errors = logData.filter(lambda s: 'ERROR' in s.split())
+{% endhighlight %}
+
+You can also pass functions that are defined using the `def` keyword; this is useful for more complicated functions that cannot be expressed using `lambda`:
+
+{% highlight python %}
+def is_error(line):
+    return 'ERROR' in line.split()
+errors = logData.filter(is_error)
+{% endhighlight %}
+
+Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated to other tasks:
+
+{% highlight python %}
+error_keywords = ["Exception", "Error"]
+def is_error(line):
+     words = line.split()
+     return any(keyword in words for keyword in error_keywords)
+errors = logData.filter(is_error)
+{% endhighlight %}
+
+PySpark will automatically ship these functions to workers, along with any objects that they reference.
+Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers.
+The [Standalone Use](#standalone-use) section describes how to ship code dependencies to workers.
+
+# Installing and Configuring PySpark
+
+PySpark requires Python 2.6 or higher.
+PySpark jobs are executed using a standard cPython interpreter in order to support Python modules that use C extensions.
+We have not tested PySpark with Python 3 or with alternative Python interpreters, such as [PyPy](http://pypy.org/) or [Jython](http://www.jython.org/).
+By default, PySpark's scripts will run programs using `python`; an alternate Python executable may be specified by setting the `PYSPARK_PYTHON` environment variable in `conf/spark-env.sh`.
+
+All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
+
+Standalone PySpark jobs should be run using the `pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh`.
+The script automatically adds the `pyspark` package to the `PYTHONPATH`.
+
+
+# Interactive Use
+
+The `pyspark` script launches a Python interpreter that is configured to run PySpark jobs.
+When run without any input files, `pyspark` launches a shell that can be used explore data interactively, which is a simple way to learn the API:
+
+{% highlight python %}
+>>> words = sc.textFile("/usr/share/dict/words")
+>>> words.filter(lambda w: w.startswith("spar")).take(5)
+[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']
+{% endhighlight %}
+
+By default, the `pyspark` shell creates SparkContext that runs jobs locally.
+To connect to a non-local cluster, set the `MASTER` environment variable.
+For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+
+{% highlight shell %}
+$ MASTER=spark://IP:PORT ./pyspark
+{% endhighlight %}
+
+
+# Standalone Use
+
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`.
+The Quick Start guide includes a [complete example](quick-start.html#a-standalone-job-in-python) of a standalone Python job.
+
+Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
+
+{% highlight python %}
+from pyspark import SparkContext
+sc = SparkContext("local", "Job Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg'])
+{% endhighlight %}
+
+Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines.
+Code dependencies can be added to an existing SparkContext using its `addPyFile()` method.
+
+# Where to Go from Here
+
+PySpark includes several sample programs using the Python API in `python/examples`.
+You can run them by passing the files to the `pyspark` script -- for example `./pyspark python/examples/wordcount.py`.
+Each example program prints usage help when run without any arguments.
+
+We currently provide [API documentation](api/pyspark/index.html) for the Python API as Epydoc.
+Many of the RDD method descriptions contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.