zhengruifeng commented on code in PR #37978:
URL: https://github.com/apache/spark/pull/37978#discussion_r979482634
##
python/pyspark/pandas/series.py:
##
@@ -6610,6 +6610,78 @@ def compare(
)
return DataFrame(internal)
+# todo: 1, support array-like 'value'; 2, add parameter 'sorter'
+def searchsorted(self, value: Any, side: str = "left") -> int:
+"""
+Find indices where elements should be inserted to maintain order.
+
+Find the indices into a sorted Series self such that, if the
corresponding elements
+in value were inserted before the indices, the order of self would be
preserved.
+
+Parameters
+--
+value : scalar
+Values to insert into self.
+side : {‘left’, ‘right’}, optional
+If ‘left’, the index of the first suitable location found is given.
+If ‘right’, return the last such index. If there is no suitable
index,
+return either 0 or N (where N is the length of self).
+
+Returns
+---
+int
+insertion point
+
+Notes
+-
+The Series must be monotonically sorted, otherwise wrong locations
will likely be returned.
+
+Examples
+
+>>> ser = ps.Series([1, 2, 2, 3])
+>>> ser.searchsorted(0)
+0
+>>> ser.searchsorted(1)
+0
+>>> ser.searchsorted(2)
+1
+>>> ser.searchsorted(5)
+4
+>>> ser.searchsorted(0, side="right")
+0
+>>> ser.searchsorted(1, side="right")
+1
+>>> ser.searchsorted(2, side="right")
+3
+>>> ser.searchsorted(5, side="right")
+4
+"""
+if side not in ["left", "right"]:
+raise ValueError(f"Invalid side {side}")
+
+sdf = self._internal.spark_frame
+index_col_name = verify_temp_column_name(sdf,
"__search_sorted_index_col__")
+value_col_name = verify_temp_column_name(sdf,
"__search_sorted_value_col__")
+sdf = InternalFrame.attach_distributed_sequence_column(
+sdf.select(self.spark.column.alias(value_col_name)), index_col_name
+)
+
+if side == "left":
+results = sdf.select(
+F.min(F.when(F.lit(value) <= F.col(value_col_name),
F.col(index_col_name))),
+F.count(F.lit(0)),
+).take(1)
+else:
+results = sdf.select(
+F.min(F.when(F.lit(value) < F.col(value_col_name),
F.col(index_col_name))),
+F.count(F.lit(0)),
+).take(1)
Review Comment:
```
In [5]: ser = pd.Series([])
:1: FutureWarning: The default dtype for empty
Series will be 'object' instead of 'float64' in a future version. Specify a
dtype explicitly to silence this warning.
ser = pd.Series([])
In [6]: ser.searchsorted(5, 'right')
Out[6]: 0
```
`Series.searchsorted` works with empty series, so here should use `take`
instead of `first` which will raise an exception.
Let me add a test case for empty series
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