CODEWITHSUNDEEP
pythonpysparkrdd
Neelhtak
Neelhtak

Reputation: 55

Python Spark Average of Tuple Values By Key

I'm trying to find the average length of words by paragraph. Data is pulled from a text file in the format of 1|For more than five years... where each line features a paragraph number.

So far this is my code:

from pyspark import SparkContext, SparkConf
sc = SparkContext('local', 'longest')
text = sc.textFile("walden.txt")
lines = text.map(lambda line: (line.split("|")[0],line))
lines = lines.filter(lambda kv: len(kv[1]) > 0)
words = lines.mapValues(lambda x: x.replace("1|","").replace("2|","").replace("3|",""))
words = words.mapValues(lambda x: x.split())
words = words.mapValues(lambda x: [(len(i),1) for i in x])
words = words.reduceByKey(lambda a,b: a+b)
words.saveAsTextFile("results")

And the current output follows this format:

('1', [(2,1),(6,1),(1,1)..etc)]),('2', [(2,1),(6,1),(1,1)..etc)]),('3', [(2,1),(6,1),(1,1)..etc)])

Where '1'/'2'/'3' are the paragraph IDs, and the tuples follow (word length, 1) format.

What I need to do is sum the values of the tuples (by key/paragraph ID) so that (2,1),(6,1),(1,1) becomes (9,3) and then divide these values (9/3) to find the average length of words in each paragraph.

I've tried a bunch of different things but just can't get this to work. Your help is greatly appreciated!

Upvotes: 0

Views: 372

Answers (1)

Lamanus
Lamanus

Reputation: 13581

For your rdd case, try this.

text = sc.textFile("test.txt")
lines = text.map(lambda line: (line.split("|")[0],line))
lines = lines.filter(lambda kv: len(kv[1]) > 0)
words = lines.mapValues(lambda x: x.replace("1|","").replace("2|","").replace("3|",""))
words = words.mapValues(lambda x: x.split())
words = words.mapValues(lambda x: [len(i) for i in x])
words = words.mapValues(lambda x: sum(x) / len(x))
words.collect()

[('1', 4.0), ('2', 5.4), ('3', 7.0)]

I use the dataframe and got this.

import pyspark.sql.functions as f

df = spark.read.option("inferSchema","true").option("sep","|").csv("test.txt").toDF("col1", "col2")
df.show(10, False)

+----+---------------------------------------+
|col1|col2                                   |
+----+---------------------------------------+
|1   |For more than five years               |
|2   |For moasdre than five asdfyears        |
|3   |Fasdfor more thasdfan fidafve yearasdfs|
+----+---------------------------------------+

df.withColumn('array', f.split('col2', r'[ ][ ]*')) \
  .withColumn('count_arr', f.expr('transform(array, x -> LENGTH(x))')) \
  .withColumn('sum', f.expr('aggregate(array, 0, (sum, x) -> sum + LENGTH(x))')) \
  .withColumn('size', f.size('array')) \
  .withColumn('avg', f.col('sum') / f.col('size')) \
  .show(10, False)

+----+---------------------------------------+---------------------------------------------+---------------+---+----+---+
|col1|col2                                   |array                                        |count_arr      |sum|size|avg|
+----+---------------------------------------+---------------------------------------------+---------------+---+----+---+
|1   |For more than five years               |[For, more, than, five, years]               |[3, 4, 4, 4, 5]|20 |5   |4.0|
|2   |For moasdre than five asdfyears        |[For, moasdre, than, five, asdfyears]        |[3, 7, 4, 4, 9]|27 |5   |5.4|
|3   |Fasdfor more thasdfan fidafve yearasdfs|[Fasdfor, more, thasdfan, fidafve, yearasdfs]|[7, 4, 8, 7, 9]|35 |5   |7.0|
+----+---------------------------------------+---------------------------------------------+---------------+---+----+---+

I know this is really different approach but would be helpful.

Upvotes: 1

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