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#
# 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,)]
df = spark.createDataFrame(data, ["id", "hour"])
# $example off$
# Output of QuantileDiscretizer for such small datasets can depend on the number of
# partitions. Here we force a single partition to ensure consistent results.
# Note this is not necessary for normal use cases
df = df.repartition(1)
# $example on$
discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result")
result = discretizer.fit(df).transform(df)
result.show()
# $example off$
spark.stop()
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