pyspark.SparkContext.parallelize¶
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SparkContext.
parallelize
(c: Iterable[T], numSlices: Optional[int] = None) → pyspark.rdd.RDD[T]¶ Distribute a local Python collection to form an RDD. Using range is recommended if the input represents a range for performance.
Examples
>>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect() [[0], [2], [3], [4], [6]] >>> sc.parallelize(range(0, 6, 2), 5).glom().collect() [[], [0], [], [2], [4]]