pyspark SparkSession及dataframe基本操作

from pyspark import SparkContext, SparkConf
import os
from pyspark.sql.session import SparkSession
from pyspark.sql import Row

def CreateSparkContex():
	sparkconf = SparkConf().setAppName("MYPRO").set("spark.ui.showConsoleProgress", "false")
	sc = SparkContext(conf=sparkconf)
	print("master:" + sc.master)
	sc.setLogLevel("WARN")
	Setpath(sc)
	spark = SparkSession.builder.config(conf=sparkconf).getOrCreate()
	return sc, spark

def Setpath(sc):
	global Path
	if sc.master[:5] == "local":
		Path = "file:/C:/spark/sparkworkspace"
	else:
		Path = "hdfs://test"



if __name__ == "__main__":
	print("Here we go!
")
	sc, spark = CreateSparkContex()
	readcsvpath = os.path.join(Path, 'iris.csv')
	dfcsv = spark.read.csv(readcsvpath, header=True,
	schema=("`Sepal.Length` DOUBLE,`Sepal.Width` DOUBLE,`Petal.Length` DOUBLE,`Petal.Width` DOUBLE,`Species` string"))
	#指定数据类型读取
	
	dfcsv.show(3)
	
	dfcsv.registerTempTable('Iris')#创建并登陆临时表
	spark.sql("select * from Iris limit 3").show()#使用sql语句查询
	spark.sql("select Species,count(1) from Iris group by Species").show()
	
	df = dfcsv.alias('Iris1')#创建一个别名
	df.select('Species', '`Sepal.Width`').show(4)#因表头有特殊字符需用反引号``转义
	df.select(df.Species,df['`Sepal.Width`']).show(4)
	dfcsv.select(df.Species).show(4)#原始名、别名的组合
	df[df.Species, df['`Sepal.Width`']].show(4)
	df[['Species']]#与pandas相同
	df['Species']#注意这是一个字段名
	
	#########增加字段
	df[df['`Sepal.Length`'], df['`Sepal.Width`'], df['`Sepal.Length`'] - df['`Sepal.Width`']].show(4)
	df[df['`Sepal.Length`'], df['`Sepal.Width`'],
	   (df['`Sepal.Length`'] - df['`Sepal.Width`']).alias('rua')].show(4)#重命名
	
	#########筛选数据
	df[df.Species == 'virginica'].show(4)#与pandas筛选一样
	df[(df.Species == 'virginica') & (df['`Sepal.Width`']>1)].show(4)#多条件筛选
	df.filter(df.Species == 'virginica').show(4)#也可以用fileter方法筛选
	spark.sql("select * from Iris where Species='virginica'").show(4)#sql筛选
	
	##########多字段排序
	spark.sql("select * from Iris order by `Sepal.Length` asc ").show(4)#升序
	spark.sql("select * from Iris order by `Sepal.Length` desc ").show(4)#降序
	spark.sql("select * from Iris order by `Sepal.Length` asc,`Sepal.Width` desc ").show(4)#升降序
	
	df.select('`Sepal.Length`', '`Sepal.Width`').orderBy('`Sepal.Width`',ascending=0).show(4)#按降序
	df.select('`Sepal.Length`', '`Sepal.Width`').orderBy('`Sepal.Width`').show(4)  # 升序
	df.select('`Sepal.Length`', '`Sepal.Width`').orderBy('`Sepal.Width`', ascending=1).show(4)  # 按升序，默认的
	df.select('`Sepal.Length`', '`Sepal.Width`').orderBy(df['`Sepal.Width`'].desc()).show(4)  # 按降序
	
	df.select('`Sepal.Length`', '`Sepal.Width`').orderBy(
		['`Sepal.Length`','`Sepal.Width`'], ascending=[0,1]).show(4)#两个字段按先降序再升序
	df.orderBy(df['`Sepal.Length`'].desc(),df['`Sepal.Width`']).show(4)
	
	##########去重
	spark.sql("select distinct Species from Iris").show()
	spark.sql("select distinct Species,`Sepal.Width` from Iris").show()
	
	df.select('Species').distinct().show()
	df.select('Species','`Sepal.Width`').distinct().show()
	df.select('Species').drop_duplicates().show()#同上，与pandas用法相同
	df.select('Species').dropDuplicates().show()#同上
	
	##########分组统计
	spark.sql("select Species,count(1) from Iris group by Species").show()
	df[['Species']].groupby('Species').count().show()
	df.groupby(['Species']).agg({'`Sepal.Width`': 'sum'}).show()
	df.groupby(['Species']).agg({'`Sepal.Width`': 'sum', '`Sepal.Length`': 'mean'}).show()
	
	#########联结数据
	dic=[['virginica','A1'],['versicolor','A2'],['setosa','A3']]
	rrd=sc.parallelize(dic)
	df2=rrd.map(lambda p: Row(lei=p[0],al=p[1]))
	df2frame=spark.createDataFrame(df2)
	df2frame.show()
	df2frame.registerTempTable('dictable')
	spark.sql("select * from Iris u left join dictable z on u.Species=z.lei").show()
	df.join(df2frame, df.Species == df2frame.lei, 'left_outer').show()
	
	sc.stop()
	spark.stop()