[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:data = [1, 2, 3, 4, 5]\ndistData = sc.parallelize(data)\ndisplay(type(distData))\nimport random\ntmp_file = "/tmp/blah" + str(random.randint(1,10000))\nrdd = sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x ))\nrdd.saveAsSequenceFile(tmp_file)\ndisplay(sorted(sc.sequenceFile(tmp_file).collect()))\ntxt_path2 = root_path + "/words.txt"\nlines = sc.textFile(txt_path2)\nlineLengths = lines.map(lambda s: len(s))\ntotalLength = lineLengths.reduce(lambda a, b: a + b)\ndisplay(totalLength)\ndef line_word_count(s):\nwords = s.split(" ")\nreturn len(words)\ndisplay(sc.textFile(txt_path2).map(line_word_count).collect())\nfrom operator import add\nlines = spark.read.text(txt_path2).rdd.map(lambda r: r[0])\ncounts = lines.flatMap(lambda x: x.split(\' \')) \\\n.map(lambda x: (x, 1)) \\\n.reduceByKey(add)\noutput = counts.collect()\nfor (word, count) in output:\nprint("%s: %i" % (word, count))\nbroadcastVar = sc.broadcast([1, 2, 3])\ndisplay(broadcastVar.value)\naccum = sc.accumulator(0)\nsc.parallelize([1, 2, 3, 4]).foreach(lambda x: accum.add(x))\ndisplay(accum.value)'}, {'role': 'assistant', 'content': "['load_data', 'data_preprocessing']"}]