code: def findFrequentSequentialPatterns(self, dataset):
          
          self._transfer_params_to_java()
          jdf = self._java_obj.findFrequentSequentialPatterns(dataset._jdf)
          return DataFrame(jdf, dataset.sql_ctx)
docs: .. note:: Experimental
  
          Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
  
          :param dataset: A dataframe containing a sequence column which is
                          `ArrayType(ArrayType(T))` type, T is the item type for the input dataset.
          :return: A `DataFrame` that contains columns of sequence and corresponding frequency.
                   The schema of it will be:
                   - `sequence: ArrayType(ArrayType(T))` (T is the item type)
                   - `freq: Long`
  
          >>> from pyspark.ml.fpm import PrefixSpan
          >>> from pyspark.sql import Row
          >>> df = sc.parallelize([Row(sequence=[[1, 2], [3]]),
          ...                      Row(sequence=[[1], [3, 2], [1, 2]]),
          ...                      Row(sequence=[[1, 2], [5]]),
          ...                      Row(sequence=[[6]])]).toDF()
          >>> prefixSpan = PrefixSpan(minSupport=0.5, maxPatternLength=5)
          >>> prefixSpan.findFrequentSequentialPatterns(df).sort("sequence").show(truncate=False)
          +----------+----+
          |sequence  |freq|
          +----------+----+
          |[[1]]     |3   |
          |[[1], [3]]|2   |
          |[[1, 2]]  |3   |
          |[[2]]     |3   |
          |[[3]]     |2   |
          +----------+----+
  
          .. versionadded:: 2.4.0