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())
words.collect()

4.去掉长度小于3的单词 filter()

words=words.filter(lambda word:len(word)>3)
words.collect()

5.去掉停用词

复制代码
with open('/home/hadoop/stopwords.txt') as f:
     stops=f.read().split()

words=words.filter(lambda word:word not in stops)
words.count()
words.collect()
复制代码

6.转换成键值对 map()

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

7.统计词频 reduceByKey()

words=words.reduceByKey(lambda a,b:a+b)

8.按字母顺序排序 sortBy(f)

words=words.sortBy(lambda word:word[0])
words.collect()

9.按词频排序 sortByKey()

words=words.sortByKey()
words.collect()

 

 

10.结果文件保存 saveAsTextFile(out_url)

words.saveAsTextFile("file:///home/hadoop/out.txt")

11.词频结果可视化charts.WordCloud()

from pyecharts.charts import WordCloud
url='D:/1342-0.txt'
with open(r'D:/stopwords.txt') as f:
    stops=f.read().split()
wc=sc.textFile(url).flatMap(lambda line:line.lower().replace(',','').split()).filter(lambda word:word not in stops).filter(lambda word:len(word)>2).map(lambda word:(word,1)).reduceByKey(lambda a,b:a+b).sortBy(lambda x:x[1],False).take(100)

mywordcloud=WordCloud()
mywordcloud.add("",wc,shape='circle')
mywordcloud.render()
 

 

 

 

二、学生课程分数案例

lines = sc.textFile('file:///home/hadoop/chapter4-data01.txt')
lines.take(5)

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[1],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[1])).countByValue()

5.Les选修了几门课?每门课多少分?filter(), map() RDD

lines.filter(lambda line:"Les" in line).map(lambda line:line.split(',')).collect()

6.Les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])).lookup("Les")
lines.map(lambda line:line.split(',')).map(lambda line:(line[0],line[2])).lookup("Les")

7.Les的成绩按分数大小排序。filter(), map(), sortBy()

lines.filter(lambda line:"Les" in line).map(lambda line:line.split(',')).sortBy(lambda line:(line[2])).collect()

 

8.Les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("Les")
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 line:line.split(',')).map(lambda line:(line[1],line[2]))
words.keys().take(5)
words.values().take(5)

10.每个分数+5分。mapValues(func)

words.mapValues(lambda x:int(x)+5).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:(int(v),1),lambda c,v:(c[0]+int(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.map(lambda x:(x[0],x[1][1],round(x[1][0]/x[1][1],2))).collect()

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)

 

 

 

 
 
 
posted on 2021-04-18 14:28  zhangxiaofeng  阅读(36)  评论(0编辑  收藏  举报