8. SparkSQL综合作业

综合练习：学生课程分数

网盘下载sc.txt文件，创建RDD，并转换得到DataFrame。

复制代码
>>> lines = spark.sparkContext.textFile('file:///home/hadoop/wc/sc.txt')
>>> parts = lines.map(lambda x:x.split(','))
>>> people = parts.map(lambda p : Row(p[0],p[1],int(p[2].strip()) ))
>>> from pyspark.sql.types import IntegerType,StringType
>>> from pyspark.sql.types import StructField,StructType
>>> from pyspark.sql import Row
>>> fields = [StructField('name',StringType(),True),StructField('course',StringType(),True), StructField('age',IntegerType(),True)]
>>> schema = StructType(fields)
>>> lines = spark.sparkContext.textFile('file:///home/hadoop/wc/sc.txt')
>>> parts = lines.map(lambda x:x.split(','))
>>> people = parts.map(lambda p : Row(p[0],p[1],int(p[2].strip()) ))
>>> schemaPeople = spark.createDataFrame(people,schema)
>>> schemaPeople.printSchema()
>>> schemaPeople.show(10)
复制代码

 

分别用DataFrame操作和spark.sql执行SQL语句实现以下数据分析:

1.总共有多少学生？

>>> schemaPeople.select(schemaPeople['name']).distinct().count()

 

2.开设了多少门课程？

>>> schemaPeople.select(schemaPeople['course']).distinct().count()

 

3.每个学生选修了多少门课？

>>> schemaPeople.groupBy('name').count().show()

 

4.每门课程有多少个学生选？

>>> schemaPeople.groupBy('course').count().show()

 

5.有多少个100分？

ilter(schemaPeople['age']==100).show()

 

6.Tom选修了几门课？每门课多少分？

>>> schemaPeople.filter(schemaPeople['name']=='Tom').show()

 

7.Tom的成绩按分数大小排序。

>>> schemaPeople.filter(schemaPeople['name']=='Tom').orderBy(schemaPeople.age).show()

 

8.Tom选修了哪几门课？

>>> schemaPeople.filter(schemaPeople['name']=='Tom').select(schemaPeople['name'],schemaPeople['course']).show()

 

9.Tom的平均分。

>>> schemaPeople.registerTempTable('people')

>>> spark.sql('select avg(age) from people where name = "Tom" ').show()

 

 

10.'OperatingSystem'不及格人数

>>> spark.sql('select count(name) from people where course = "OperatingSystem" and age < 60').show()

 

11.'OperatingSystem'平均分

>>> spark.sql('select avg(age) from people where course = "OperatingSystem" ').show()

 

12.'OperatingSystem'90分以上人数

>>> spark.sql('select * from people where course = "OperatingSystem" and age > 90 ').show()

 

13.每个分数按比例+20平时分。

>>> schemaPeople.select(schemaPeople['name'],schemaPeople['course'],schemaPeople['age']+20).show(5)

 

14.求每门课的平均分

>>> spark.sql('select course, AVG(age) from people where GROUP BY course').show()

 

15.求每门课的最高分最低分

>>> spark.sql('select course, MAX(age) from people where GROUP BY course').show()

 

 
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