0.前次作业：从文件创建DataFrame

 

1.pandas df 与 spark df的相互转换

df_s=spark.createDataFrame(df_p)

df_p=df_s.toPandas()

>>> import pandas as pd
>>> import numpy as np
>>> arr = np.arange(6).reshape(-1,3)
>>> df_p=pd.DataFrame(arr)
>>> df_p

>>> arr

>>> df_p.columns=['a','b','c']
>>> df_p

 

 

 

 

 

>>> df_s=spark.createDataFrame(df_p)
>>> df_s.show()

>>> df_s.collect()

>>> df_s.toPandas()

 

 

 

2. Spark与Pandas中DataFrame对比

http://www.lining0806.com/spark%E4%B8%8Epandas%E4%B8%ADdataframe%E5%AF%B9%E6%AF%94/

 

3.1 利用反射机制推断RDD模式

• sc创建RDD
• 转换成Row元素，列名=值
• spark.createDataFrame生成df
• df.show(), df.printSchema()

>>> from pyspark.sql import Row
>>> people = spark.sparkContext.textFile("file:///usr/local/spark/examples/src/main/resources/people.txt").map(lambda line:line.split(',')).map(lambda p:Row(name=p[0],age=int(p[1])))
>>> schemaPeople=spark.createDataFrame(people)
>>> schemaPeople.createOrReplaceTempView("people")
>>> personsDF=spark.sql("select name,age from people where age>20")
>>> personsRDD=personsDF.rdd.map(lambda p:"Name:"+p.name+","+"Age:"+str(p.age))
>>> personsRDD.foreach(print)

 

 

 

>>> schemaPeople.show()

>>> schemaPeople.printSchema()

 

 

 

 

3.2 使用编程方式定义RDD模式

• 生成“表头”
  • fields = [StructField(field_name, StringType(), True) ,...]
  • schema = StructType(fields)

>>> from pyspark.sql.types import StringType,StructField,StructType
>>> from pyspark.sql import Row
>>> schemaString = "name age"
>>> fields = [StructField(field_name,StringType(),True) for field_name in schemaString.split(" ")]
>>> schema = StructType(fields)

>>> fields

>>> schema

 

 

 

• 生成“表中的记录”
  • 创建RDD
  • 转换成Row元素，列名=值

>>> lines = spark.sparkContext.textFile("file:///usr/local/spark/examples/src/main/resources/people.txt")
>>> parts = lines.map(lambda x:x.split(","))
>>> people = parts.map(lambda p:Row(p[0],p[1].strip()))
>>> people.collect()

 

 

 

• 把“表头”和“表中的记录”拼装在一起
  • = spark.createDataFrame(RDD, schema)

>>> schemaPeople = spark.createDataFrame(people,schema)
>>> schemaPeople.show()

>>> schemaPeople.printSchema()

 

 

 4. DataFrame保存为文件

df.write.json(dir)

>>> schemaPeople.write.json("file///home/hadoop/schema_out")