code: def convert_sparse_to_dataframe(spark, context, sparse_matrix):
      """Converts a scipy sparse matrix to a spark dataframe"""
      m = sparse_matrix.tocoo()
      data = context.parallelize(
          numpy.array([m.row, m.col, m.data]).T,
          numSlices=len(m.row) / 1024,
      )
      return spark.createDataFrame(
          data.map(
              lambda p: Row(
                  row=int(p[0]), col=int(p[1]), data=float(p[2])
              )
          )
      )
  
file_path: implicit/benchmarks/benchmark_spark.py
imports: import argparse
  import json
  import time
  import matplotlib.pyplot
  import numpy
  import scipy.io
  import seaborn
  from pyspark import SparkConf, SparkContext
  from pyspark.ml.recommendation import ALS
  from pyspark.sql import Row, SparkSession
  import implicit
label_desc: Use image node_id 1 for calling a global function with example usage: convert_sparse_to_dataframe(spark, context, sparse_matrix) and returns: spark