如何将字典列表转换为 Pyspark DataFrame [英] How to convert list of dictionaries into Pyspark DataFrame

问题描述

我想将我的字典列表转换为 DataFrame.这是清单:

I want to convert my list of dictionaries into DataFrame. This is the list:

mylist = 
[
  {"type_activity_id":1,"type_activity_name":"xxx"},
  {"type_activity_id":2,"type_activity_name":"yyy"},
  {"type_activity_id":3,"type_activity_name":"zzz"}
]

这是我的代码:

from pyspark.sql.types import StringType

df = spark.createDataFrame(mylist, StringType())

df.show(2,False)

+-----------------------------------------+
|                                    value|
+-----------------------------------------+
|{type_activity_id=1,type_activity_id=xxx}|
|{type_activity_id=2,type_activity_id=yyy}|
|{type_activity_id=3,type_activity_id=zzz}|
+-----------------------------------------+

我假设我应该为每一列提供一些映射和类型，但我不知道该怎么做.

I assume that I should provide some mapping and types for each column, but I don't know how to do it.

更新:

我也试过这个:

schema = ArrayType(
    StructType([StructField("type_activity_id", IntegerType()),
                StructField("type_activity_name", StringType())
                ]))
df = spark.createDataFrame(mylist, StringType())
df = df.withColumn("value", from_json(df.value, schema))

但后来我得到 null 值:

+-----+
|value|
+-----+
| null|
| null|
+-----+

推荐答案

你可以这样做.您将获得一个包含 2 列的数据框.

You can do it like this. You will get a dataframe with 2 columns.

mylist = [
  {"type_activity_id":1,"type_activity_name":"xxx"},
  {"type_activity_id":2,"type_activity_name":"yyy"},
  {"type_activity_id":3,"type_activity_name":"zzz"}
]

myJson = sc.parallelize(mylist)
myDf = sqlContext.read.json(myJson)

输出:

+----------------+------------------+
|type_activity_id|type_activity_name|
+----------------+------------------+
|               1|               xxx|
|               2|               yyy|
|               3|               zzz|
+----------------+------------------+

这篇关于如何将字典列表转换为 Pyspark DataFrame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！