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)

posted @ 2021-05-13 00:00  隔壁老尤  阅读(25)  评论(0编辑  收藏  举报