1.pandas df 与 spark df的相互转换
df_s=spark.createDataFrame(df_p)
df_p=df_s.toPandas()
|
1
2
3
|
import pandas as pdimport numpy as nparr = np.arange(6).reshape(-1,3) |
|
1
2
|
df_p=pd.DataFrame(arr)df_p |
|
1
2
|
df_p.columns=[‘a‘,‘b‘,‘c‘]df_p |
|
1
2
|
df_s=spark.createDataFrame(df_p)df_s.show() |
|
1
|
df_s.collect() |
|
1
|
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
|
1
2
3
4
|
from pyspark.sql import Rowpeople = spark.sparkContext.textFile(‘file:///usr/local/spark/examples/src/main/resources/people.txt‘).map(lambda line:line.split(‘,‘)).map(lambda w:Row(name=w[0],age=int(w[1])))sPeople = spark.createDataFrame(people)sPeople.createOrReplaceTempView(‘people‘) |
|
1
2
3
|
personDF = spark.sql(‘select name,age from people where age>20‘)personRDD = personDF.rdd.map(lambda p:"Name:"+p.name+","+"Age:"+str(p.age))personRDD.foreach(print) |
|
1
|
sPeople.show() |
|
1
|
sPeople.printSchema() |
3.2 使用编程方式定义RDD模式
|
1
2
3
4
|
from pyspark.sql.types import *from pyspark.sql import RowschemaString = ‘name age‘fields = [StructField(field_name,StringType(),True) for field_name in schemaString.split(‘ ‘)]<br>schema = StructType(fields) |
|
1
2
3
4
|
lines = spark.sparkContext.textFile(‘file:///usr/local/spark/examples/src/main/resources/people.txt‘)part = lines.map(lambda w:w.split(","))peoples = part.map(lambda p:Row(p[0],p[1].strip()))peoples.collect() |
|
1
2
3
|
schemaPeople = spark.createDataFrame(people,schema)schemaPeople.show()schemaPeople.printSchema() |
4. DataFrame保存为文件
df.write.json(dir)
|
1
|
schemaPeople.write.json(‘file:///home/hadoop/schema_out‘) |
原文：https://www.cnblogs.com/thebao/p/14770032.html