itholic commented on code in PR #37801:
URL: https://github.com/apache/spark/pull/37801#discussion_r965455458
##########
python/pyspark/pandas/groupby.py:
##########
@@ -895,6 +895,95 @@ def sem(col: Column) -> Column:
bool_to_numeric=True,
)
+ # TODO: 1, 'n' accepts list and slice; 2, implement 'dropna' parameter
+ def nth(self, n: int) -> FrameLike:
+ """
+ Take the nth row from each group.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ n : int
+ A single nth value for the row
+
+ Returns
+ -------
+ Series or DataFrame
+
+ Examples
+ --------
+ >>> df = ps.DataFrame({'A': [1, 1, 2, 1, 2],
+ ... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
+ >>> g = df.groupby('A')
+ >>> g.nth(0)
+ B
+ A
+ 1 NaN
+ 2 3.0
+ >>> g.nth(1)
+ B
+ A
+ 1 2.0
+ 2 5.0
+ >>> g.nth(-1)
+ B
+ A
+ 1 4.0
+ 2 5.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+ """
+ if not isinstance(n, int):
+ raise TypeError("Unsupported type %s" % type(n).__name__)
Review Comment:
Yes, so we can:
- Raise `TypeError` for unsupported type in pandas as well.
- Raise `NotImplementedError` which is not implemented only in pandas API on
Spark, but existing in pandas.
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