michaelzhan-db commented on code in PR #42637:
URL: https://github.com/apache/spark/pull/42637#discussion_r1309069098
##########
python/pyspark/sql/dataframe.py:
##########
@@ -4850,6 +4850,40 @@ def approxQuantile(
-----
Null values will be ignored in numerical columns before calculation.
For columns only containing null values, an empty list is returned.
+
+ Examples
+ --------
+ Example 1: Calculating quantiles for a single column
+
+ >>> data = [(1,), (2,), (3,), (4,), (5,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 5.0]
+
+ Example 2: Calculating quantiles for multiple columns
+
+ >>> data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
+ >>> df = spark.createDataFrame(data, ["col1", "col2"])
+ >>> quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0],
0.05)
+ >>> quantiles
+ [[1.0, 10.0], [3.0, 30.0], [5.0, 50.0]]
+
+ Example 3: Handling null values
+
+ >>> data = [(1,), (None,), (3,), (4,), (None,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 4.0]
+
+ Example 4: Calculating quantiles with different target precision
Review Comment:
Good idea! I'll update the example description
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
