[SPARK-14512] [DOC] Add python example for QuantileDiscretizer

## What changes were proposed in this pull request?
Add the missing python example for QuantileDiscretizer

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12281 from zhengruifeng/discret_pe.

docs/ml-features.md

@@ -1118,6 +1118,15 @@ for more details on the API.
 
 {% include_example java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [QuantileDiscretizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.QuantileDiscretizer)
+for more details on the API.
+
+{% include_example python/ml/quantile_discretizer_example.py %}
+</div>
+
 </div>
 
 # Feature Selectors

examples/src/main/python/ml/quantile_discretizer_example.py

@@ -0,0 +1,39 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.feature import QuantileDiscretizer
+# $example off$
+from pyspark.sql import SparkSession
+
+
+if __name__ == "__main__":
+    spark = SparkSession.builder.appName("PythonQuantileDiscretizerExample").getOrCreate()
+
+    # $example on$
+    data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
+    dataFrame = spark.createDataFrame(data, ["id", "hour"])
+
+    discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result")
+
+    result = discretizer.fit(dataFrame).transform(dataFrame)
+    result.show()
+    # $example off$
+
+    spark.stop()