diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 7c5283fb0b6fb..5b3e95833a282 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -6,7 +6,7 @@ title: Python Programming Guide
 
 The Spark Python API (PySpark) exposes the Spark programming model to Python.
 To learn the basics of Spark, we recommend reading through the
-[Scala programming guide](scala-programming-guide.html) first; it should be
+[Scala programming guide](scala-programming-guide.md) 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.
 
@@ -45,7 +45,7 @@ errors = logData.filter(is_error)
 
 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.
+The [Standalone Use](#standalone-programs) section describes how to ship code dependencies to workers.
 
 In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
 
@@ -84,7 +84,7 @@ The Python shell can be used explore data interactively and is a simple way to l
 
 By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on a single core.
 To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable.
-For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.md):
 
 {% highlight bash %}
 $ MASTER=spark://IP:PORT ./bin/pyspark
@@ -120,7 +120,7 @@ IPython also works on a cluster or on multiple cores if you set the `MASTER` env
 # Standalone Programs
 
 PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/pyspark`.
-The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application.
+The Quick Start guide includes a [complete example](quick-start.md#a-standalone-app-in-python) of a standalone Python application.
 
 Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
 
@@ -132,8 +132,8 @@ sc = SparkContext("local", "App Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg
 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.
 
-You can set [configuration properties](configuration.html#spark-properties) by passing a
-[SparkConf](api/pyspark/pyspark.conf.SparkConf-class.html) object to SparkContext:
+You can set [configuration properties](configuration.md#spark-properties) by passing a
+[SparkConf](http://spark.apache.org/docs/latest/api/pyspark/pyspark.conf.SparkConf-class.html) object to SparkContext:
 
 {% highlight python %}
 from pyspark import SparkConf, SparkContext
@@ -146,7 +146,7 @@ sc = SparkContext(conf = conf)
 
 # API Docs
 
-[API documentation](api/pyspark/index.html) for PySpark is available as Epydoc.
+[API documentation](http://spark.apache.org/docs/latest/api/pyspark/index.html) for PySpark is available as Epydoc.
 Many of the methods also contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.
 
 # Libraries