请尝试以下操作：

df = (
    df
    .withColumn('value', f.sum(f.col('value')).over(Window.partitionBy(f.col('ID'))))
)

指向有关Window操作https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.window.html的文档的链接

您可以使用简单的groupBy和sum函数：

from pyspark.sql import functions as F

(
    df
    .groupby("ID", 'Gender')  # sum rows with same ID and Gender
    # .groupby("ID")  # use this line instead if you want to sum rows with the same ID, even if they have different Gender
    .agg(F.sum('value').alias('value'))
)

其结果是：

+---+------+-----+
| ID|Gender|value|
+---+------+-----+
|  1|  Male|   11|
|  2|Female|    3|
|  3|Female|    9|
|  4|  Male|   10|
+---+------+-----+