Question

鉴于以下数据框，我如何调整最高分数但总计播放总和？

from pyspark import SparkContext
from pyspark.sql import HiveContext
from pyspark.sql import functions as F
from pyspark.sql import Window

df = sqlContext.createDataFrame([
    ("u1", "g1", 10, 0, 1),
    ("u1", "g3", 2, 2, 1),
    ("u1", "g3", 5, 3, 1),
    ("u1", "g4", 5, 4, 1),
    ("u2", "g2", 1, 1, 1),
], ["UserID", "GameID", "Score", "Time", "Plays"])

所需输出

+------+-------------+-------------+-----+
|UserID|MaxScoreGame1|MaxScoreGame2|Plays|
+------+-------------+-------------+-----+
|    u1|           10|            5|    4|
|    u2|            1|         null|    1|
+------+-------------+-------------+-----+

我在下面发布了一个解决方案，但我希望避免使用加入。

Answer 1

我认为这不是一个真正的改进，但你可以添加总数

...
.select(
  F.col("*"),
  F.row_number().over(rowNumberWindow).alias("GameNumber"),
  F.sum("Plays").over(rowNumberWindow.orderBy()).alias("total_plays")
)
...

稍后将其用作pivot的辅助分组列：

...
.groupBy("UserID", "total_plays")
.pivot("GameCol", ["MaxScoreGame1", "MaxScoreGame2"])
.agg(F.max("Score"))
...

Answer 2

以下是使用加入的解决方案，我希望避免这样做：

汇总数据框

df_sum = df.groupBy("UserID").agg(F.sum("Plays").alias("Plays")).alias("df_sum")
df_sum.show()

+------+-----+
|UserID|Plays|
+------+-----+
|    u1|    4|
|    u2|    1|
+------+-----+

rowNumberWindow = Window.partitionBy("UserID").orderBy(F.col("Time"))

透视数据框

rowNumberWindow = Window.partitionBy("UserID").orderBy(F.col("Time"))

df_piv = (df
         .groupBy("UserID", "GameID")
         .agg(F.sum("Plays").alias("Plays"),
              F.max("Score").alias("Score"),
              F.min("Time").alias("Time"))
         .select(F.col("*"),
                 F.row_number().over(rowNumberWindow).alias("GameNumber"))
         .filter(F.col("GameNumber") <= F.lit(2))
         .withColumn("GameCol", F.concat(F.lit("MaxScoreGame"), F.col("GameNumber")))
         .groupBy("UserID")
         .pivot("GameCol", ["MaxScoreGame1", "MaxScoreGame2"])  
         .agg(F.max("Score"))
        ).alias("df_piv")

df_piv.show()

+------+-------------+-------------+
|UserID|MaxScoreGame1|MaxScoreGame2|
+------+-------------+-------------+
|    u1|           10|            5|
|    u2|            1|         null|
+------+-------------+-------------+

加入数据框

df_joined = df_sum.join(df_piv, F.col("df_sum.UserID") == F.col("df_piv.UserID"))

df_joined.show()

+------+-----+------+-------------+-------------+
|UserID|Plays|UserID|MaxScoreGame1|MaxScoreGame2|
+------+-----+------+-------------+-------------+
|    u1|    4|    u1|           10|            5|
|    u2|    1|    u2|            1|         null|
+------+-----+------+-------------+-------------+

Spark旋转一列，但保持其他完整

2 个答案: