zero323 commented on a change in pull request #27278:
[SPARK-30569][SQL][PYSPARK][SPARKR] Add percentile_approx DSL functions.
URL: https://github.com/apache/spark/pull/27278#discussion_r368509689
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File path: python/pyspark/sql/functions.py
##########
@@ -582,6 +582,51 @@ def nanvl(col1, col2):
return Column(sc._jvm.functions.nanvl(_to_java_column(col1),
_to_java_column(col2)))
+@since(3.0)
+def percentile_approx(col, percentage, accuracy=10000):
+ """Returns the approximate percentile value of numeric column col at the
given percentage.
+ The value of percentage must be between 0.0 and 1.0.
+
+ The accuracy parameter (default: 10000)
+ is a positive numeric literal which controls approximation accuracy at the
cost of memory.
+ Higher value of accuracy yields better accuracy, 1.0/accuracy is the
relative error
+ of the approximation.
+
+ When percentage is an array, each value of the percentage array must be
between 0.0 and 1.0.
+ In this case, returns the approximate percentile array of column col
+ at the given percentage array.
+
+ >>> df = (spark.range(0, 1000, 1, 1)
+ ... .withColumn("id", col("id") % 3)
+ ... .withColumn("value", randn(42) + col("id") * 10))
+ >>> (df
+ ... .select(percentile_approx("value", [0.25, 0.5, 0.75],
1000000).alias("quantiles"))
+ ... .show(truncate=False))
+ +----------------------------------------------------------+
+ |quantiles |
+ +----------------------------------------------------------+
+ |[0.7264430125286507, 9.98975299938167, 19.335304783039014]|
Review comment:
Maybe `printSchema`? I think the main point is to show that schema can
differ from input to input.
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