下面是一个可能的解决方案（不是最优雅的，但仍然有效）。
在窗口定义中，使用.rowsBetween创建指定size的滑动窗口;0表示当前行。

import pyspark.sql.functions as F
from pyspark.sql.window import Window

# parameters
size = 3
id_padding = '\'\''
label_padding = 'f'

# windows
w = Window.partitionBy('group')
w_ordered = Window.partitionBy('group').orderBy('id')
w_ordered_limited = Window.partitionBy('group').orderBy('id').rowsBetween(0, size - 1)

(df.select(
  'group',
  F.collect_list('id').over(w_ordered_limited).alias('Windows'),
  F.collect_list('Label').over(w_ordered_limited).alias('Groups'),
  F.count('group').over(w).alias('n'),
  F.row_number().over(w_ordered).alias('n_row')
  )
  # pad arrays and then slice them to the desired `size`
  .withColumn('Windows', F.when(F.col('n') < size, F.slice(F.concat('Windows', F.array_repeat(F.lit(id_padding), size - 1)), 1, size))
                          .otherwise(F.col('Windows')))
  .withColumn('Groups',  F.when(F.col('n') < size, F.slice(F.concat('Groups', F.array_repeat(F.lit(label_padding), size - 1)), 1, size))
                          .otherwise(F.col('Groups')))
  # filter out useless rows
  .filter( ((F.col('n') < size) & (F.col('n_row') == 1)) 
           | ((F.col('n') >= size) & (F.size('Windows') == size)))
  .drop('n', 'n_row')
 ).show()

+-----+----------+---------+
|group|   Windows|   Groups|
+-----+----------+---------+
|    1|[A, B, '']|[T, T, f]|
|    2|[C, D, '']|[F, F, f]|
|    3| [E, F, G]|[F, T, F]|
|    3| [F, G, H]|[T, F, T]|
+-----+----------+---------+

我建议您一步一步地浏览解决方案，一次一行代码，以理解其背后的逻辑。

为了扩展@Ric S的答案，我还需要考虑步幅。
我的解决方案是根据步幅值及其与win_size的比率来处理条件和变换：

import pyspark.sql.functions as F
from pyspark.sql.window import Window

# parameters
size = 5
stride = 2
id_padding = '\'\''
label_padding = 'f'

# windows
w = Window.partitionBy('group')
w_ordered = Window.partitionBy('group').orderBy('id')
w_ordered_limited = Window.partitionBy('group').orderBy('id').rowsBetween(0, size - 1)

if stride == 1:
  filter_cond = (F.col('n') >= size) & (F.size('Windows') == size)
else:
  filter_cond = (F.col('n') >= size) & (F.col('n_row') < size) & (F.col('n_row') % stride == 1) & (F.size('Windows') == size)

if size % stride != 0:
  transf = lambda dfcol : F.slice(F.concat(dfcol, F.array_repeat(F.lit(id_padding), size - 1)), 1, size)
else:
  transf = lambda dfcol : F.when(F.col('n') < size, F.slice(F.concat(dfcol, F.array_repeat(F.lit(id_padding), size - 1)), 1, size)) \
                          .otherwise(F.col(dfcol))
(df.select(
  'group',
  F.collect_list('id').over(w_ordered_limited).alias('Windows'),
  F.collect_list('Label').over(w_ordered_limited).alias('Groups'),
  F.count('group').over(w).alias('n'),
  F.row_number().over(w_ordered).alias('n_row')
  )
  # pad arrays and then slice them to the desired `size`
  .withColumn('Windows', transf("Windows"))
  .withColumn('Groups',  transf("Groups"))
  # filter out useless rows
  .filter( ((F.col('n') < size) & (F.col('n_row') == 1)) 
           | (filter_cond))
  .drop('n', 'n_row')
 ).show()

如果输入比问题中给出的输入稍大，则其输出为：

+-----+------------------+------------------+
|group|           Windows|            Groups|
+-----+------------------+------------------+
|    1|[A, B, '', '', '']|[T, T, '', '', '']|
|    2|[C, D, '', '', '']|[F, F, '', '', '']|
|    3|   [E, F, G, H, I]|   [F, T, F, T, T]|
|    3|  [G, H, I, J, '']|  [F, T, T, T, '']|
+-----+------------------+------------------+