diff --git a/docs/quick-start.md b/docs/quick-start.md
index b88ae5f6bb313..cb5211af377e5 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -66,6 +66,11 @@ res3: Long = 15
 
     ./bin/pyspark
 
+
+Or if PySpark is installed with pip in your current enviroment:
+
+    pyspark
+
 Spark's primary abstraction is a distributed collection of items called a Dataset. Datasets can be created from Hadoop InputFormats (such as HDFS files) or by transforming other Datasets. Due to Python's dynamic nature, we don't need the Dataset to be strongly-typed in Python. As a result, all Datasets in Python are Dataset[Row], and we call it `DataFrame` to be consistent with the data frame concept in Pandas and R. Let's make a new DataFrame from the text of the README file in the Spark source directory:
 
 {% highlight python %}
@@ -206,7 +211,7 @@ a cluster, as described in the [RDD programming guide](rdd-programming-guide.htm
 
 # Self-Contained Applications
 Suppose we wish to write a self-contained application using the Spark API. We will walk through a
-simple application in Scala (with sbt), Java (with Maven), and Python.
+simple application in Scala (with sbt), Java (with Maven), and Python (pip).
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -367,6 +372,16 @@ Lines with a: 46, Lines with b: 23
 
 Now we will show how to write an application using the Python API (PySpark).
 
+
+If you are building a packaged PySpark application or library you can add it to your setup.py file as:
+
+{% highlight python %}
+    install_requires=[
+        'pyspark=={site.SPARK_VERSION}'
+    ]
+{% endhighlight %}
+
+
 As an example, we'll create a simple Spark application, `SimpleApp.py`:
 
 {% highlight python %}
@@ -406,6 +421,16 @@ $ YOUR_SPARK_HOME/bin/spark-submit \
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}
 
+If you have PySpark pip installed into your enviroment (e.g. `pip instal pyspark` you can run your application with the regular Python interpeter or use the provided spark-submit as you prefer.
+
+{% highlight bash %}
+# Use spark-submit to run your application
+$ python SimpleApp.py
+...
+Lines with a: 46, Lines with b: 23
+{% endhighlight %}
+
+
 </div>
 </div>
 
diff --git a/docs/rdd-programming-guide.md b/docs/rdd-programming-guide.md
index 0966d3870e8f8..c0215c8fb62f6 100644
--- a/docs/rdd-programming-guide.md
+++ b/docs/rdd-programming-guide.md
@@ -89,7 +89,18 @@ import org.apache.spark.SparkConf;
 Spark {{site.SPARK_VERSION}} works with Python 2.7+ or Python 3.4+. It can use the standard CPython interpreter,
 so C libraries like NumPy can be used. It also works with PyPy 2.3+.
 
-To run Spark applications in Python, use the `bin/spark-submit` script located in the Spark directory.
+Python 2.6 support was removed in Spark 2.2.0.
+
+Spark applications in Python can either be run with the `bin/spark-submit` script which includes Spark at runtime, or by including including it in your setup.py as:
+
+{% highlight python %}
+    install_requires=[
+        'pyspark=={site.SPARK_VERSION}'
+    ]
+{% endhighlight %}
+
+
+To run Spark applications in Python without pip installing PySpark, use the `bin/spark-submit` script located in the Spark directory.
 This script will load Spark's Java/Scala libraries and allow you to submit applications to a cluster.
 You can also use `bin/pyspark` to launch an interactive Python shell.