如何將行顯示為 pyspark 數據框中的字典？

Question

pyspark 的新手。

我有 2 個數據集， Events和Gadget 。 他們看起來像這樣：

Events

Gadgets

我可以像這樣使用來讀取和加入 2 個數據幀，並在我的最后一行中只顯示所需的列：

import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType,StructField, StringType, IntegerType 
from pyspark.sql.types import ArrayType, DoubleType, BooleanType
from pyspark.sql.functions import col,array_contains

spark = SparkSession.builder.appName('PySpark Read CSV').getOrCreate()

# Reading csv file
events = spark.read.option("header",True).csv("events.csv")
events.printSchema()


gadgets = spark.read.option("header",True).csv("gadgets.csv")
gadgets.printSchema()


enrich = events.join(gadgets, events.deviceId == gadgets.ID).select(events["*"],gadgets["User"])

我的任務是要求我在字典對象中像這樣呈現數據：

充實任務：

• 使用設備提供的用戶數據豐富事件對象。
• 確保豐富的事件如下所示：

{
    sessionId: string
    deviceId: string
    timestamp: timestamp
    type: emun(ADDED_TO_CART | APP_OPENED)
    total_price: 50.00
    user: string
}

我可以處理分配要求的 dtype 更改和列名重命名，但是如何以上面的字典格式提供我的結果？

如果我使用此行，我不確定如何顯示我的結果：

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

Answer 1

使用create_map()函數創建每列及其值的（鍵，值）對。

create_map需要以形式輸入 (key1, value1, key2, value2, ...)。 為此，請使用itertools.chain() 。

df = spark.createDataFrame(data=[["sess1","dev1","2022-12-19","emun(ADDED_TO_CART | APP_OPENED)","50.00","usr1"],["sess2","dev2","2022-12-18","emun(ADDED_TO_CART | APP_OPENED)","100.00","usr2"]], schema=["sessionId","deviceId","timestamp","type","total_price","user"])

import pyspark.sql.functions as F
import itertools

df = df.withColumn("map", \
                   F.create_map( \
                       list(itertools.chain( \
                           *((F.lit(x), F.col(x)) for x in df.columns) \
                       )) \
                   ))

df.show(truncate=False)

輸出：

+---------+--------+----------+--------------------------------+-----------+----+----------------------------------------------------------------------------------------------------------------------------------------------+
|sessionId|deviceId|timestamp |type                            |total_price|user|map                                                                                                                                           |
+---------+--------+----------+--------------------------------+-----------+----+----------------------------------------------------------------------------------------------------------------------------------------------+
|sess1    |dev1    |2022-12-19|emun(ADDED_TO_CART | APP_OPENED)|50.00      |usr1|{sessionId -> sess1, deviceId -> dev1, timestamp -> 2022-12-19, type -> emun(ADDED_TO_CART | APP_OPENED), total_price -> 50.00, user -> usr1} |
|sess2    |dev2    |2022-12-18|emun(ADDED_TO_CART | APP_OPENED)|100.00     |usr2|{sessionId -> sess2, deviceId -> dev2, timestamp -> 2022-12-18, type -> emun(ADDED_TO_CART | APP_OPENED), total_price -> 100.00, user -> usr2}|
+---------+--------+----------+--------------------------------+-----------+----+----------------------------------------------------------------------------------------------------------------------------------------------+

您還可以使用以下方法將其收集為json ：

df = df.withColumn("json", F.to_json("map"))