Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/7554#discussion_r35244037
--- Diff: docs/mllib-data-types.md ---
@@ -431,7 +454,42 @@ long n = mat.numCols();
// Drop its row indices.
RowMatrix rowMat = mat.toRowMatrix();
{% endhighlight %}
-</div></div>
+</div>
+
+<div data-lang="python" markdown="1">
+
+An
[`IndexedRowMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.IndexedRowMatrix)
can be created from an
+`RDD` of IndexedRows, where
[`IndexedRow`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.IndexedRow)
is a
+wrapper over `(long, Vector)`. An `IndexedRowMatrix` can be converted to
a `RowMatrix` by dropping its row indices.
--- End diff --
Yeah, although it doesn't matter for Python 3, Python 2 still has the
distinction, and regardless, from an API/documentation perspective, I think
it's a good idea to mimic the requirements of the Scala side, which uses a long
in that case.
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