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-rw-r--r--examples/src/main/python/ml/quantile_discretizer_example.py11
1 files changed, 8 insertions, 3 deletions
diff --git a/examples/src/main/python/ml/quantile_discretizer_example.py b/examples/src/main/python/ml/quantile_discretizer_example.py
index 6ae7bb18f8..5444cacd95 100644
--- a/examples/src/main/python/ml/quantile_discretizer_example.py
+++ b/examples/src/main/python/ml/quantile_discretizer_example.py
@@ -28,11 +28,16 @@ if __name__ == "__main__":
# $example on$
data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
- dataFrame = spark.createDataFrame(data, ["id", "hour"])
-
+ 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(dataFrame).transform(dataFrame)
+ result = discretizer.fit(df).transform(df)
result.show()
# $example off$