[STREAMING][DOC][MINOR] Update the description of direct Kafka stream doc

With the merge of [SPARK-8337](https://issues.apache.org/jira/browse/SPARK-8337), now the Python API has the same functionalities compared to Scala/Java, so here changing the description to make it more precise.

zsxwing tdas , please review, thanks a lot.

Author: jerryshao <[email protected]>

Closes #10246 from jerryshao/direct-kafka-doc-update.

(cherry picked from commit 24d3357)
Signed-off-by: Shixiong Zhu <[email protected]>

Author: jerryshao

docs/streaming-kafka-integration.md

@@ -74,7 +74,7 @@ Next, we discuss how to use this approach in your streaming application.
 	[Maven repository](http://search.maven.org/#search|ga|1|a%3A%22spark-streaming-kafka-assembly_2.10%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) and add it to `spark-submit` with `--jars`.
 
 ## Approach 2: Direct Approach (No Receivers)
-This new receiver-less "direct" approach has been introduced in Spark 1.3 to ensure stronger end-to-end guarantees. Instead of using receivers to receive data, this approach periodically queries Kafka for the latest offsets in each topic+partition, and accordingly defines the offset ranges to process in each batch. When the jobs to process the data are launched, Kafka's simple consumer API is used to read the defined ranges of offsets from Kafka (similar to read files from a file system). Note that this is an experimental feature introduced in Spark 1.3 for the Scala and Java API. Spark 1.4 added a Python API, but it is not yet at full feature parity.
+This new receiver-less "direct" approach has been introduced in Spark 1.3 to ensure stronger end-to-end guarantees. Instead of using receivers to receive data, this approach periodically queries Kafka for the latest offsets in each topic+partition, and accordingly defines the offset ranges to process in each batch. When the jobs to process the data are launched, Kafka's simple consumer API is used to read the defined ranges of offsets from Kafka (similar to read files from a file system). Note that this is an experimental feature introduced in Spark 1.3 for the Scala and Java API, in Spark 1.4 for the Python API.
 
 This approach has the following advantages over the receiver-based approach (i.e. Approach 1).