在 Pyspark 中压平 Json

Question

my_data=[
    {'stationCode': 'NB001',
       'summaries': [{'period': {'year': 2017}, 'rainfall': 449},
        {'period': {'year': 2018}, 'rainfall': 352.4},
        {'period': {'year': 2019}, 'rainfall': 253.2},
        {'period': {'year': 2020}, 'rainfall': 283},
        {'period': {'year': 2021}, 'rainfall': 104.2}]},
    {'stationCode': 'NA003',
       'summaries': [{'period': {'year': 2019}, 'rainfall': 58.2},
        {'period': {'year': 2020}, 'rainfall': 628.2},
        {'period': {'year': 2021}, 'rainfall': 120}]}]

在 Pandas 中我可以：

import pandas as pd
from pandas import json_normalize
pd.concat([json_normalize(entry, 'summaries', 'stationCode') 
                     for entry in my_data])

这会给我下表：

    rainfall  period.year stationCode
0     449.0         2017       NB001
1     352.4         2018       NB001
2     253.2         2019       NB001
3     283.0         2020       NB001
4     104.2         2021       NB001
0      58.2         2019       NA003
1     628.2         2020       NA003
2     120.0         2021       NA003

pyspark中一行代码可以实现这一点吗？

我已经尝试过下面的代码，它给了我相同的结果。但是，它太长了，有办法缩短吗？

df=sc.parallelize(my_data)
df1=spark.read.json(df)


  df1.select("stationCode","summaries.period.year","summaries.rainfall").display()
  df1 = df1.withColumn("year_rainfall", F.arrays_zip("year", "rainfall"))
           .withColumn("year_rainfall", F.explode("year_rainfall"))
           .select("stationCode", 
               F.col("year_rainfall.rainfall").alias("Rainfall"), 
               F.col("year_rainfall.year").alias("Year"))
  df1.display(20, False)

向 pyspark 自我介绍，因此我们将非常感谢一些解释或良好的信息来源

Answer 1

你所拥有的对我来说看起来不错并且可读。不过你也可以直接压缩并爆炸：

out = (df1.select("stationCode", 
      F.explode(F.arrays_zip(*["summaries.period.year","summaries.rainfall"])))
.select("stationCode",F.col("col")['0'].alias("year"),F.col("col")['1'].alias("rainfall")))

out.show()

+-----------+----+--------+
|stationCode|year|rainfall|
+-----------+----+--------+
|      NB001|2017|   449.0|
|      NB001|2018|   352.4|
|      NB001|2019|   253.2|
|      NB001|2020|   283.0|
|      NB001|2021|   104.2|
|      NA003|2019|    58.2|
|      NA003|2020|   628.2|
|      NA003|2021|   120.0|
+-----------+----+--------+