[GitHub] [spark] Yikun commented on a diff in pull request #37816: [SPARK-40332][PS] Implement `GroupBy.quantile`

[GitBox <gi...@apache.org>]

Yikun commented on code in PR #37816:
URL: https://github.com/apache/spark/pull/37816#discussion_r964909876


##########
python/pyspark/pandas/groupby.py:
##########
@@ -581,6 +581,56 @@ def mean(self, numeric_only: Optional[bool] = True) -> FrameLike:
             F.mean, accepted_spark_types=(NumericType,), bool_to_numeric=True
         )
 
+    # TODO: 'q' accepts list like type
+    def quantile(self, q: float = 0.5) -> FrameLike:
+        """
+        Return group values at the given quantile.
+
+        .. note:: `quantile` in pandas-on-Spark are using distributed percentile approximation
+        algorithm unlike pandas, the result might different with pandas in accuracy, also
+        `interpolation` parameters are not supported yet.
+
+        Parameters
+        ----------
+        q : float, default 0.5 (50% quantile)
+            Value between 0 and 1 providing the quantile to compute.
+
+            .. versionadded:: 3.4.0
+
+        Returns
+        -------
+        pyspark.pandas.Series or pyspark.pandas.DataFrame
+
+        See Also
+        --------
+        pyspark.pandas.Series.quantile
+        pyspark.pandas.DataFrame.quantile
+        pyspark.sql.functions.percentile_approx
+
+        Examples
+        --------
+        >>> df = ps.DataFrame([
+        ...     ['a', 1], ['a', 2], ['a', 3],
+        ...     ['b', 1], ['b', 3], ['b', 5]
+        ... ], columns=['key', 'val'])
+
+        Groupby one column and return the quantile of the remaining columns in
+        each group.
+
+        >>> df.groupby('key').quantile()
+             val
+        key
+        a    2
+        b    3
+        """
+        if is_list_like(q):

Review Comment:
   pandas also support this, or did I missing something?
   
   https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.quantile.html#pandas-core-groupby-dataframegroupby-quantile



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