05 RDD编程2
一、词频统计:
1.读文本文件生成RDD lines
2.将一行一行的文本分割成单词 words flatmap()
3.全部转换为小写 lower()
4.去掉长度小于3的单词 filter()
5.去掉停用词
6.转换成键值对 map()
7.统计词频 reduceByKey()
8.按字母顺序排序 sortBy(f)
9.按词频排序 sortByKey()
10. 结果文件保存 saveAsTextFile(out_url)
11.词频结果可视化charts.WordCloud()
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import jieba
with open('t4','r',encoding='utf-8') as f:
text = f.read()
cut_text = " ".join(jieba.cut(text))
cloud = WordCloud(
background_color="white",
max_words=200,
# font_path="C:\Windows\Fonts\STLITI.TTF",
font_path="C:\Windows\Fonts\stxingka.ttf",
min_font_size=20,
max_font_size=50,
width=800,
height=400
)
wCloud = cloud.generate(cut_text)
plt.imshow(wCloud,interpolation='bilinear')
plt.axis('off')
plt.show()
二、学生课程分数案例
- 总共有多少学生?map(), distinct(), count()
- 开设了多少门课程?
- 每个学生选修了多少门课?map(), countByKey()
- 每门课程有多少个学生选?map(), countByValue()
- Tom选修了几门课?每门课多少分?filter(), map() RDD\
- Tom的成绩按分数大小排序。filter(), map(), sortBy()
- Tom选修了几门课?每门课多少分?map(),lookup() list
- Tom的平均分。map(),lookup(),mean()
- 生成(课程,分数)RDD,观察keys(),values()
- 每个分数+5分。mapValues(func)
- 求每门课的选修人数及所有人的总分。combineByKey()
- 求每门课的选修人数及平均分,精确到2位小数。map(),round()
- 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。
- 结果可视化。charts,Bar()
import matplotlib.pyplot as plt
data = [51.9,50.91,50.54,48.83,54.94,57.82,47.57,50.61]
labels = ["ComputerNetwork","Software","DataBase","Algorithm","OperatingSystem","Python","DataStructure","CLanguage"]
plt.xticks(rotation=45)
plt.bar(range(len(data)), data,tick_label=labels)
plt.show()