如何在pyspark中将行转换为Dictionary? [英] How to convert rows into Dictionary in pyspark?

问题描述

通过从配置单元表中读取，我在pyspark中有一个DataFrame(df):

I have a DataFrame(df) in pyspark, by reading from a hive table:

df=spark.sql('select * from <table_name>')


+++++++++++++++++++++++++++++++++++++++++++
|  Name    |    URL visited               |
+++++++++++++++++++++++++++++++++++++++++++
|  person1 | [google,msn,yahoo]           |
|  person2 | [fb.com,airbnb,wired.com]    |
|  person3 | [fb.com,google.com]          |
+++++++++++++++++++++++++++++++++++++++++++

当我尝试以下操作时，出现错误

When i tried the following, got an error

df_dict = dict(zip(df['name'],df['url']))
"TypeError: zip argument #1 must support iteration."

type(df.name) is of 'pyspark.sql.column.Column'

我如何创建类似以下的字典，以后可以对其进行迭代

How do i create a dictionary like the following, which can be iterated later on

{'person1':'google','msn','yahoo'}
{'person2':'fb.com','airbnb','wired.com'}
{'person3':'fb.com','google.com'}

感谢您的想法和帮助.

Appreciate your thoughts and help.

推荐答案

我认为您可以尝试row.asDict()，此代码直接在执行程序上运行，而您不必在驱动程序上收集数据.

I think you can try row.asDict(), this code run directly on the executor, and you don't have to collect the data on driver.

类似的东西:

df.rdd.map(lambda row: row.asDict())

这篇关于如何在pyspark中将行转换为Dictionary?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！