There’s a DataFrame in pyspark with data as below:
user_id object_id score user_1 object_1 3 user_1 object_1 1 user_1 object_2 2 user_2 object_1 5 user_2 object_2 2 user_2 object_2 6
What I expect is returning 2 records in each group with the same user_id, which need to have the highest score. Consequently, the result should look as the following:
user_id object_id score user_1 object_1 3 user_1 object_2 2 user_2 object_2 6 user_2 object_1 5
I’m really new to pyspark, could anyone give me a code snippet or portal to the related documentation of this problem? Great thanks!
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Answer
I believe you need to use window functions to attain the rank of each row based on user_id
and score
, and subsequently filter your results to only keep the first two values.
from pyspark.sql.window import Window from pyspark.sql.functions import rank, col window = Window.partitionBy(df['user_id']).orderBy(df['score'].desc()) df.select('*', rank().over(window).alias('rank')) .filter(col('rank') <= 2) .show() #+-------+---------+-----+----+ #|user_id|object_id|score|rank| #+-------+---------+-----+----+ #| user_1| object_1| 3| 1| #| user_1| object_2| 2| 2| #| user_2| object_2| 6| 1| #| user_2| object_1| 5| 2| #+-------+---------+-----+----+
In general, the official programming guide is a good place to start learning Spark.
Data
rdd = sc.parallelize([("user_1", "object_1", 3), ("user_1", "object_2", 2), ("user_2", "object_1", 5), ("user_2", "object_2", 2), ("user_2", "object_2", 6)]) df = sqlContext.createDataFrame(rdd, ["user_id", "object_id", "score"])