[GitHub] [spark] michaelzhan-db commented on a diff in pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring

["michaelzhan-db (via GitHub)" <gi...@apache.org>]

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
   



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