给定这些数据帧：

IncomingCount
-------------------------
Venue|Date    | 08 | 10 |
-------------------------
Hotel|20190101| 15 | 03 |
Beach|20190101| 93 | 45 |

OutgoingCount
-------------------------
Venue|Date    | 07 | 10 | 
-------------------------
Beach|20190101| 30 | 5  |
Hotel|20190103| 05 | 15 |

我如何可以合并(完全联接)两个表,从而得到如下结果而不必手动遍历两个表的每一行？

Dictionary:
[
 {"Venue":"Hotel", "Date":"20190101", "08":{ "IncomingCount":15 }, "10":{ "IncomingCount":03 } },
 {"Venue":"Beach", "Date":"20190101", "07":{ "OutgoingCount":30 }, "08":{ "IncomingCount":93 }, "10":{ "IncomingCount":45, "OutgoingCount":15 } },
 {"Venue":"Hotel", "Date":"20190103", "07":{ "OutgoingCount":05 }, "10":{ "OutgoingCount":15 } }
]

条件是：

>“地点”和“日期”列的作用类似于加入条件.
>其他以数字表示的列是动态创建的.
>如果动态列不存在,则将其排除(或以None作为值包含在内).

解决方法:

这很奇怪,但是可以通过使用spark中的create_map函数来完成.

基本上将列分为四组：键(地点,日期),通用键(10),仅传入键(08),仅传出键(07).

然后按组(键除外)创建映射器,仅映射每个组可用的映射器.应用映射,删除旧列,然后将映射的列重命名为旧名称.

最后将所有行转换为dict(来自df的rdd)并收集.

from pyspark.sql import SparkSession
from pyspark.sql.functions import create_map, col, lit

spark = SparkSession.builder.appName('hotels_and_beaches').getOrCreate()

incoming_counts = spark.createDataFrame([('Hotel', 20190101, 15, 3), ('Beach', 20190101, 93, 45)], ['Venue', 'Date', '08', '10']).alias('inc')
outgoing_counts = spark.createDataFrame([('Beach', 20190101, 30, 5), ('Hotel', 20190103, 5, 15)], ['Venue', 'Date', '07', '10']).alias('out')

df = incoming_counts.join(outgoing_counts, on=['Venue', 'Date'], how='full')

outgoing_cols = {c for c in outgoing_counts.columns if c not in {'Venue', 'Date'}}
incoming_cols = {c for c in incoming_counts.columns if c not in {'Venue', 'Date'}}

common_cols = outgoing_cols.intersection(incoming_cols)

outgoing_cols = outgoing_cols.difference(common_cols)
incoming_cols = incoming_cols.difference(common_cols)

for c in common_cols:
    df = df.withColumn(
        c + '_new', create_map(
            lit('IncomingCount'), col('inc.{}'.format(c)),
            lit('OutgoingCount'), col('out.{}'.format(c)),
        )
    ).drop(c).withColumnRenamed(c + '_new', c)

for c in incoming_cols:
    df = df.withColumn(
        c + '_new', create_map(
            lit('IncomingCount'), col('inc.{}'.format(c)),
        )
    ).drop(c).withColumnRenamed(c + '_new', c)

for c in outgoing_cols:
    df = df.withColumn(
        c + '_new', create_map(
            lit('OutgoingCount'), col('out.{}'.format(c)),
        )
    ).drop(c).withColumnRenamed(c + '_new', c)

result = df.coalesce(1).rdd.map(lambda r: r.asDict()).collect()
print(result)

结果：

[{'Venue': 'Hotel', 'Date': 20190101, '10': {'OutgoingCount': None, 'IncomingCount': 3}, '08': {'IncomingCount': 15}, '07': {'OutgoingCount': None}}, {'Venue': 'Hotel', 'Date': 20190103, '10': {'OutgoingCount': 15, 'IncomingCount': None}, '08': {'IncomingCount': None}, '07': {'OutgoingCount': 5}}, {'Venue': 'Beach', 'Date': 20190101, '10': {'OutgoingCount': 5, 'IncomingCount': 45}, '08': {'IncomingCount': 93}, '07': {'OutgoingCount': 30}}]