05 RDD编程
一、词频统计:
1.读文本文件生成RDD lines
>>> lines = sc.textFile('file:///home/hadoop/word.txt')
2.将一行一行的文本分割成单词 words flatmap()
>>> words=lines.flatMap(lambda line:line.split())
>>> words.collect()
3.全部转换为小写 lower()
>>> words=lines.flatMap(lambda line:line.lower().split()).collect()
>>> words=lines.flatMap(lambda line:line.lower().split())
>>> words.collect()
4.去掉长度小于3的单词 filter()
>>> words.filter(lambda word : len(word)>3).collect()
5.去掉停用词
>>> lines = sc.textFile('file:///home/hadoop/stopwords.txt')
>>> stop = lines.flatMap(lambda line : line.split()).collect()
>>> lines = sc.textFile('file:///home/hadoop/word.txt')
>>> words=lines.flatMap(lambda line:line.lower().split()).filter(lambda word : word not in stop)
>>> words.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).foreach(print)
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()
10.结果文件保存 saveAsTextFile(out_url)
lines = sc.textFile('file:///home/hadoop/chapter4-data01.txt') course_rev = lines.map(lambda line:line.split(',')).map(lambda x:(x[1],(int(x[2]),1))).reduceByKey(lambda a,b:(a[0]+b[0],a[1]+b[1])) course_rev.saveAsTextFile("file:///home/hadoop/out_url")
二、学生课程分数案例
1.总共有多少学生?map(), distinct(), count()
>>> lines.map(lambda line : line.split(',')[0]).distinct().count()
2.开设了多少门课程?
>>> lines.map(lambda line : line.split(',')[1]).distinct().count()
3.每个学生选修了多少门课?map(), countByKey()
>>> lines.map(lambda line : line.split(',')).map(lambda line:(line[0],line[2])).countByKey()
4.每门课程有多少个学生选?map(), countByValue()
>>> lines.map(lambda line : line.split(',')).map(lambda line : (line[0])).countByValue()
5。Tom选修了几门课?每门课多少分?filter(), map() RDD
>>> lines.filter(lambda line:"Tom" in line).map(lambda line:line.split(',')).collect()
6.Tom选修了几门课?每门课多少分?map(),lookup() list
>>> Tom=lines.filter(lambda line:'Tom' in line).map(lambda line:line.split(' '))
>>> Tom.collect()
7.Tom的成绩按分数大小排序。filter(), map(), sortBy()
>>> lines.filter(lambda line:"Tom" in line).map(lambda line:line.split(',')).sortBy(lambda line:(line[2])).collect()
8.Tom的平均分。map(),lookup(),mean()
>>> import numpy as np >>> meanlist=lines.map(lambda line:line.split(',')).map(lambda line:(line[0],line[2])).lookup("Tom") >>> np.mean([int(x) for x in meanlist])
9. 生成(课程,分数)RDD,观察keys(),values()
>>> lines = sc.textFile('file:///home/hadoop/chapter4-data01.txt') >>> words = lines.map(lambda x:x.split(',')).map(lambda x:(x[1],x[2])) >>> words.keys().take(5) >>> words.values().take(5)
10.每个分数+5分。mapValues(func)
>>> words = words.map(lambda x:(x[0],int(x[1]))) >>> words.mapValues(lambda x:x+1).foreach(print)
11.求每门课的选修人数及所有人的总分。combineByKey()
>>>course = words.combineByKey(lambda v:(v,1),lambda c,v:(c[0]+v,c[1]+1),lambda c1,c2:(c1[0]+c2[0],c1[1]+c2[1]))
12.求每门课的选修人数及平均分,精确到2位小数。map(),round()
>>>course_rev = course.map(lambda x:(x[0],x[1][1],round(x[1][0]/x[1][1])))
13.求每门课的选修人数及平均分。用reduceByKey()实现,并比较与combineByKey()的异同。
>>> lines.map(lambda line:line.split(',')).map(lambda x:(x[1],(int(x[2]),1))).reduceByKey(lambda a,b:(a[0]+b[0],a[1]+b[1])).foreach(print)