Github user xubo245 commented on a diff in the pull request: https://github.com/apache/spark/pull/20456#discussion_r165838751 --- Diff: python/pyspark/sql/dataframe.py --- @@ -667,6 +667,51 @@ def repartition(self, numPartitions, *cols): else: raise TypeError("numPartitions should be an int or Column") + @since("2.3.0") + def repartitionByRange(self, numPartitions, *cols): + """ + Returns a new :class:`DataFrame` partitioned by the given partitioning expressions. The + resulting DataFrame is range partitioned. + + ``numPartitions`` can be an int to specify the target number of partitions or a Column. + If it is a Column, it will be used as the first partitioning column. If not specified, + the default number of partitions is used. + + At least one partition-by expression must be specified. + When no explicit sort order is specified, "ascending nulls first" is assumed. + + >>> df.repartitionByRange(2, "age").rdd.getNumPartitions() + 2 + >>> df.show() + +---+-----+ + |age| name| + +---+-----+ + | 2|Alice| + | 5| Bob| + +---+-----+ + >>> df.repartitionByRange(1, "age").rdd.getNumPartitions() + 1 + >>> data = df.repartitionByRange("age") + >>> df.show() + +---+-----+ + |age| name| + +---+-----+ + | 2|Alice| + | 5| Bob| + +---+-----+ + """ + if isinstance(numPartitions, int): + if len(cols) == 0: + return ValueError("At least one partition-by expression must be specified.") + else: + return DataFrame( + self._jdf.repartitionByRange(numPartitions, self._jcols(*cols)), self.sql_ctx) + elif isinstance(numPartitions, (basestring, Column)): + cols = (numPartitions,) + cols + return DataFrame(self._jdf.repartitionByRange(self._jcols(*cols)), self.sql_ctx) + else: + raise TypeError("numPartitions should be an int or Column") --- End diff -- ok, done
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