Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2819#discussion_r19436372
--- Diff: docs/mllib-feature-extraction.md ---
@@ -95,8 +95,50 @@ tf.cache()
val idf = new IDF(minDocFreq = 2).fit(tf)
val tfidf: RDD[Vector] = idf.transform(tf)
{% endhighlight %}
+</div>
+<div data-lang="python" markdown="1">
+
+TF and IDF are implemented in
[HashingTF](api/python/pyspark.mllib.html#pyspark.mllib.feature.HashingTF)
+and [IDF](api/python/pyspark.mllib.html#pyspark.mllib.feature.IDF).
+`HashingTF` takes an RDD of list as the input.
+Each record could be an iterable of strings or other types.
+
+{% highlight python %}
+from pyspark import SparkContext
+from pyspark.mllib.linalg import Vector
+from pyspark.mllib.feature import HashingTF
+
+sc = SparkContext()
+
+# Load documents (one per line).
+documents = sc.textFile("...").map(lambda line: line.split(" "))
+
+hashingTF = HashingTF()
+tf = hashingTF.transform(documents)
+{% endhighlight %}
+
+While applying `HashingTF` only needs a single pass to the data, applying
`IDF` needs two passes:
+first to compute the IDF vector and second to scale the term frequencies
by IDF.
+
+{% highlight python %}
+from pyspark.mllib.feature import IDF
+
+# ... continue from the previous example
+tf.cache()
+idf = IDF().fit(tf)
+tfidf = idf.transform(tf)
+{% endhighlight %}
+MLLib's IDF implementation provides an option for ignoring terms which
occur in less than a
+minimum number of documents. In such cases, the IDF for these terms is
set to 0. This feature
+can be used by passing the `minDocFreq` value to the IDF constructor.
+{% highlight python %}
+# ... continue from the previous example
+tf.cache()
+idf = IDF().fit(tf)
--- End diff --
This is supposed to be an example of explicitly setting minDocFreq. Please
compare with the Scala example.
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