我在PySpark中有以下 Dataframe ：

from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.appName("myApp").getOrCreate()

# Create a list of rows for the DataFrame
rows = [("2011-11-13 11:00", 1, "2011-11-13 11:06", "2011-11-14 11:00"),
        ("2011-11-13 11:01", 1, "2011-11-13 11:06", "2011-11-14 11:00"),
        ("2011-11-13 11:04", 1, "2011-11-13 11:06", "2011-11-14 11:00"),
        ("2011-11-13 11:15", 1, "2011-11-13 11:06", "2011-11-14 11:00"),
        ("2011-12-15 15:00", 2, "2011-12-15 15:05", "2012-01-02 15:00"),
        ("2011-12-15 15:06", 2, "2011-12-15 15:05", "2012-01-02 15:00"),
        ("2011-12-15 15:08", None, None, None)]

# Create a DataFrame from the rows
df = spark.createDataFrame(rows, ["timestamp", "myid", "start_timestamp", "end_timestamp"])

# Show the DataFrame
df.show()

打印输出：

+-------------------+----+-------------------+---------------------+
|          timestamp|myid|   start_timestamp |       end_timestamp |
+-------------------+----+-------------------+---------------------+
|2011-11-13 11:00:00|   1|2011-11-13 11:06:00|2011-11-14 11:00:00  |
|2011-11-13 11:01:00|   1|2011-11-13 11:06:00|2011-11-14 11:00:00  |
|2011-11-13 11:04:00|   1|2011-11-13 11:06:00|2011-11-14 11:00:00  |
|2011-11-13 11:15:00|   1|2011-11-13 11:06:00|2011-11-14 11:00:00  |
|2011-12-15 15:00:00|   2|2011-12-15 15:05:00|2012-01-02 15:00:00  |
|2011-12-15 15:06:00|   2|2011-12-15 15:05:00|2012-01-02 15:00:00  |
|2011-12-15 15:08:00|null|               null|                null |
|2011-12-17 16:00:00|null|               null|                null |
+-------------------+----+-------------------+---------------------+

我只需要选择符合以下条件的行：
1.在“开始时间戳”和“结束时间戳”中具有空值，或
1.具有与“start_timestamp”列值最接近的“timestamp”。
对于上面的示例，预期结果如下：

+--------------------+------------+----------------------+---------------------+
|timestamp           |     myid   |  start_timespamp     |     end_timestamp
+--------------------+------------+----------------------+---------------------+
|2011-11-13 11:04    |    1       |  2011-11-13 11:06    |   2011-11-14  11:00
|2011-12-15 15:06    |    2       |  2011-12-15 15:05    |   2012-01-02  15:00
|2011-12-15 15:08    |    null    |  null                |   null
|2011-12-17 16:00:00 |    null    |  null                |   null
+--------------------+------------+----------------------+---------------------+

这是我当前的代码，但它给出了错误的结果：

此外，在我的ral数据集的情况下，此代码失败，错误为Aggregate/Window/Generate expressions are not valid in where clause of the query：

from pyspark.sql import Window
from pyspark.sql.functions import abs, col, min

# Create a window to partition the data by "myid" and order by "timestamp"
window = Window.partitionBy("myid").orderBy("timestamp")

# Add a new column "time_diff" that calculates the absolute difference between "timestamp" and "start_timestamp"
df = df.withColumn("time_diff", abs(col("timestamp").cast("long") - col("start_timestamp").cast("long")))

# Add a new column "min_time_diff" that contains the minimum "time_diff" for each "myid"
df = df.withColumn("min_time_diff", min("time_diff").over(window))

# Select the rows that have null values in "start_timespamp" and "end_timestamp"
# or have the minimum value in "time_diff" for each "myid"
df = df.filter((col("start_timestamp").isNull() & col("end_timestamp").isNull()) | 
              (col("time_diff") == col("min_time_diff")))