dgd-contributor commented on a change in pull request #33858:
URL: https://github.com/apache/spark/pull/33858#discussion_r700322747
##########
File path: python/pyspark/pandas/series.py
##########
@@ -4425,6 +4427,127 @@ def replace(
return self._with_new_scol(current) # TODO: dtype?
+ def combine(
+ self,
+ other: "Series",
+ func: Callable,
+ fill_value: Optional[Any] = None,
+ return_type: Optional[Union[str, DataType]] = "string",
+ ) -> "Series":
+ """
+ Combine the Series with a Series or scalar according to `func`.
+ Combine the Series and `other` using `func` to perform elementwise
+ selection for combined Series.
+ `fill_value` is assumed when value is missing at some index
+ from one of the two objects being combined.
+
+ Parameters
+ ----------
+ other : Series or scalar
+ The value(s) to be combined with the `Series`.
+ func : function
+ Function that takes two scalars as inputs and returns an element.
+ fill_value : scalar, optional
+ The value to assume when an index is missing from
+ one Series or the other. The default specifies to use the
+ appropriate NaN value for the underlying dtype of the Series.
+ return_type : :class:`pyspark.sql.types.DataType` or str
+ the return type of the output Series. The value can be either a
+ :class:`pyspark.sql.types.DataType` object or a DDL-formatted type
string.
+
+ Returns
+ -------
+ Series
+ The result of combining the Series with the other object.
+
+ See Also
+ --------
+ Series.combine_first : Combine Series values, choosing the calling
+ Series' values first.
+
+ Examples
+ --------
+ Consider 2 Datasets ``s1`` and ``s2`` containing
+ highest clocked speeds of different birds.
+
+ >>> from pyspark.pandas.config import set_option, reset_option
+ >>> set_option("compute.ops_on_diff_frames", True)
+ >>> s1 = ps.Series({'falcon': 330.0, 'eagle': 160.0})
+ >>> s1
+ falcon 330.0
+ eagle 160.0
+ dtype: float64
+ >>> s2 = ps.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
+ >>> s2
+ falcon 345.0
+ eagle 200.0
+ duck 30.0
+ dtype: float64
+
+ Now, to combine the two datasets and view the highest speeds
+ of the birds across the two datasets
+
+ >>> s1.combine(s2, max, return_type="double")
+ duck NaN
+ eagle 200.0
+ falcon 345.0
+ dtype: float64
+
+ In the previous example, the resulting value for duck is missing,
+ because the maximum of a NaN and a float is a NaN.
+ So, in the example, we set ``fill_value=0``,
+ so the maximum value returned will be the value from some dataset.
+
+ >>> s1.combine(s2, max, fill_value=0, return_type="double")
+ duck 30.0
+ eagle 200.0
+ falcon 345.0
+ dtype: float64
+ >>> reset_option("compute.ops_on_diff_frames")
+ """
+ if not isinstance(other, Series) and not np.isscalar(other):
+ raise TypeError("unsupported type: %s" % type(other))
+
+ if np.isscalar(other):
+ combined = DataFrame(
+ InternalFrame(
+
spark_frame=self._internal.spark_frame.withColumn("__other", SF.lit(other)),
+ index_spark_columns=self._internal.index_spark_columns,
+ )
+ )
+ else:
+ combined = (
+ self._psdf[self._column_label, other._column_label]
+ if same_anchor(self, other)
+ else combine_frames(self.to_frame(), other.to_frame())
+ )
+
+ sdf = combined._internal.spark_frame
+ if fill_value is not None:
+ sdf = sdf.fillna(fill_value)
+
+ @udf
Review comment:
As I know, the func argument in pandas Series.combine takes two scalars
as inputs and returns an element and the pandas user defined function should
have inputs and output type are pd.Series or pd.Dataframe. So I do not know how
to wrap the func argument in pandas Series.combine with pandas_udf.
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