Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8279#discussion_r37427896
--- Diff: docs/mllib-frequent-pattern-mining.md ---
@@ -133,6 +134,36 @@ for (AssociationRules.Rule<String> rule
{% endhighlight %}
</div>
+
+<div data-lang="python" markdown="1">
+
+[`FPGrowth`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth)
implements the
+FP-growth algorithm.
+It take an `RDD` of transactions, where each transaction is an `List` of
items of a generic type.
+Calling `FPGrowth.train` with transactions returns an
+[`FPGrowthModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowthModel)
+that stores the frequent itemsets with their frequencies. The following
+example illustrates how to mine frequent itemsets and association rules
+(see [Association
+Rules](mllib-frequent-pattern-mining.html#association-rules) for
+details) from `transactions`.
+
+{% highlight python %}
+from pyspark.mllib.fpm import FPGrowth
+
+data = sc.textFile("data/mllib/sample_fpgrowth.txt")
+
+transactions = data.map(lambda line: line.strip().split(' '))
+
+model = FPGrowth.train(transactions, 0.2, 10)
--- End diff --
Use named arguments.
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