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!
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')
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"])
Top-n is more accurate if using row_number instead of rank when getting rank equality:
val n = 5 df.select(col('*'), row_number().over(window).alias('row_number')) \ .where(col('row_number')
Note limit(20).toPandas() trick instead of show() for Jupyter notebooks for nicer formatting.
