07 从RDD创建DataFrame

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

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:///home/hadoop/chapter4-data01.txt')
  >>>people=spark.sparkContext.textFile('file:///home/hadoop/chapter4-data01.txt').map(lambda line:line.split(',')).map(lambda p:Row(name=p[0],course=p[1],score=int(p[2])))
  >>> df=spark.createDataFrame(people)
  >>> df.first()
  Row(course='OperatingSystem', name='Aaron', score=100)
  >>> df.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 *
    >>> from pyspark.sql import Row
    >>> schemaString='name course score'
    >>> fields=[StructField(field_name,StringType(),True) for field_name in schemaString.split(' ')]
    >>> schema=StructType(fields)

    

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

  • >>>lines=spark.sparkContext.textFile('file:///home/hadoop/chapter4-data01.txt')
    >>> parts=lines.map(lambda x:x.split(','))
    >>> people=parts.map(lambda p:Row(p[0],p[1],p[2].strip()))

    

• 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()

    

 4. DataFrame保存为文件

df.write.json(dir)

>>> dir='file:///home/hadoop/sqlrdd'
>>> df.write.json(dir)