如何在pyspark中设置计数id的日期间隔？

Question

我有一个 pyspark 数据框，其列 parsed_date (dtype: date) 和 id (dtype: bigint) 如下所示：

+-------+-----------+
|     id|parsed_date|
+-------+-----------+
|1471783| 2017-12-18|
|1471885| 2017-12-18|
|1472928| 2017-12-19|
|1476917| 2017-12-19|
|1477469| 2017-12-21|
|1478190| 2017-12-21|
|1478570| 2017-12-19|
|1481415| 2017-12-21|
|1472592| 2017-12-20|
|1474023| 2017-12-22|
|1474029| 2017-12-22|
+-------+-----------+

我有一个如下所示的函数。目的是传递一个日期（天）和 t（天数）。在 df1 中，id 计入范围（day-t，day）中，在 df2 中，id 计入范围（day，day+t）中。

from pyspark.sql import functions as F, Window

def hypo_1(df, day, t):

    df1 = (df.filter(f"parsed_date between '{day}' - interval {t} days and '{day}'")
             .withColumn('count_before', F.count('id').over(Window.partitionBy('parsed_date')))
             .orderBy('parsed_date')
          )
    df2 = (df.filter(f"parsed_date between '{day}' and '{day}' + interval {t} days")
             .withColumn('count_after', F.count('id').over(Window.partitionBy('parsed_date')))
             .orderBy('parsed_date')
          )
    return [df1, df2]

使用此代码，函数返回两个数据帧：

示例：hypo_1(df, '2017-12-20', 2)

df1

+-----------+-------+------------+
|parsed_date|     id|count_before|
+-----------+-------+------------+
| 2017-12-20|1471783|           1|
+-----------+-------+------------+

df2

+-----------+-------+-----------+
|parsed_date|     id|count_after| 
+-----------+-------+-----------+
| 2017-12-20|1472592|          1|
| 2017-12-21|1477469|          3|
| 2017-12-22|1474029|          2|
+-----------+-------+-----------+

问题：

1. df1 的日期间隔看起来不正确。

2. 不应该计算我通过的日期 (2017-12-20) 的 id，这在 df1 和 df2 中都发生 ->

    +-----------+-------+-----------+
    |parsed_date|     id|count_after| 
    +-----------+-------+-----------+
    | 2017-12-20|1472592|          1|
   

预期输出：

示例：hypo_1(df, '2017-12-20', 2)

df1:

+-------+-----------+------------+
|     id|parsed_date|count_before|
+-------+-----------+------------+
|1471783| 2017-12-18|           2|
|1478570| 2017-12-19|           3|
+-------+-----------+------------+

df2:

+-------+-----------+------------+
|     id|parsed_date| count_after|
+-------+-----------+------------+
|1477469| 2017-12-21|           3|
|1474023| 2017-12-22|           2|
+-------+-----------+------------+

请帮忙。

Answer 1

只需稍微更改您的过滤条件（添加- interval 1 day 或+ interval 1 day）：

from pyspark.sql import functions as F, Window

def hypo_1(df, day, t):
    df1 = (df.filter(f"parsed_date between '{day}' - interval {t} days and '{day}' - interval 1 day")
             .withColumn('count_before', F.count('id').over(Window.partitionBy('parsed_date')))
             .orderBy('parsed_date')
          )
    df2 = (df.filter(f"parsed_date between '{day}' + interval 1 day and '{day}' + interval {t} days")
             .withColumn('count_after', F.count('id').over(Window.partitionBy('parsed_date')))
             .orderBy('parsed_date')
          )
    return [df1, df2]

df1, df2 = hypo_1(df, '2017-12-20', 2)
df1.show()
+-------+-----------+------------+
|     id|parsed_date|count_before|
+-------+-----------+------------+
|1471783| 2017-12-18|           2|
|1471885| 2017-12-18|           2|
|1472928| 2017-12-19|           3|
|1476917| 2017-12-19|           3|
|1478570| 2017-12-19|           3|
+-------+-----------+------------+

df2.show()
+-------+-----------+-----------+
|     id|parsed_date|count_after|
+-------+-----------+-----------+
|1481415| 2017-12-21|          3|
|1478190| 2017-12-21|          3|
|1477469| 2017-12-21|          3|
|1474023| 2017-12-22|          2|
|1474029| 2017-12-22|          2|
+-------+-----------+-----------+

如果你想得到你想要的输出，你可以删除重复，例如

df1 = df1.dropDuplicates(['parsed_date', 'count_after'])