05 RDD编程

一、词频统计：

1.读文本文件生成RDD lines

lines=sc.textFile("file:///usr/local/spark/mycode/rdd/word.txt")

lines.foreach(print)

 

 

2.将一行一行的文本分割成单词 words flatmap()

words=lines.flatMap(lambda line:line.split())

words.foreach(print)

 

 

3.全部转换为小写 lower()

wordsxx=lines.map(lambda word:word.lower())

wordsxx.foreach(print)

 

 

4.去掉长度小于3的单词 filter()

word=words.filter(lambda words:len(words)>2)

word.foreach(print)

 

 

5.去掉停用词

lines=textFile("file:///usr/local/spark/mycode/rdd/word.txt")

with open("/usr/lcaol/spark/mycode/rdd/stopwords.txt") as f:

      stops=f.read().split()

lines.flatMap(lambda line:line.split()).filter(lambda word:word not in stops).collect()

 

6.转换成键值对 map()

words.map(lambda word:(word,1)).collect()

 

7.统计词频 reduceByKey()

words.map(lambda word:(word,1)).reduceByKey(lambda a,b:a+b).collect()

 

8.按字母顺序排序 sortBy(f)

words.map(lambda word:(word,1)).reduceByKey(lambda a,b:a+b).sortBy(lambda word:word[0]).collect()

 

 

9.按词频排序 sortByKey()

words.map(lambda word:(word,1)).reduceByKey(lambda a,b:a+b).sortByKey().collect()

 

 

二、学生课程分数案例

lines=sc.textFile("file:///usr/local/spark/mycode/rdd/xs.txt")

• 总共有多少学生？map(), distinct(), count()

lines.map(lambda line:line.split(',')[0]).distinct().count()

• 开设了多少门课程？

lines.map(lambda line:line.split(',')[1]).distinct().count()

• 每个学生选修了多少门课？map(), countByKey()

lines.map(lambda line:line.split(',')).map(lambda line:(ine[0],line[1])).countByKey()

• 每门课程有多少个学生选？map(), countByValue()

lines.map(lambda line:line.split(',')).map(lambda line:line[1]).countByValue()

• Tom选修了几门课？每门课多少分？filter(), map() RDD

lines.file(lambda line:"Tom" in line).map(lambda line:line.split(','))

• Tom选修了几门课？每门课多少分？map(),lookup()  list

lines.map(lambda line:line.split(',')).map(lambda line:(line[0],(line[1],line[2]))).lookup('Tom')

• Tom的成绩按分数大小排序。filter(), map(), sortBy()

lines.filter(lambda line:'Tom' in line).map(lambda line:l;ine.split(',')).sortBy(lambda line:line[2],False).collect()

• Tom的平均分。map(),lookup(),mean()

import numpy as np

name=lines.map(lambda line:line.split(',')).map(lambda line:(line[0],(line[1],line[2]))).lookup('Tom')

np.mean([int(x) for x in name])