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PySpark is a powerful tool for big data processing that allows users to efficiently manipulate and analyze large datasets. One useful feature of PySpark is the ability to select the row with the maximum value in each group. This allows for the identification of top performers or outliers within a dataset, providing valuable insights for further analysis. By utilizing PySpark’s groupby and agg functions, users can easily group their data and extract the maximum value for each group, making it a convenient and efficient method for data analysis.
PySpark: Select Row with Max Value in Each Group
You can use the following syntax to select the row with the max value by group in a PySpark DataFrame:
from pyspark.sql import Window import pyspark.sql.functions as F #specify column to group by w = Window.partitionBy('team') #find row with max value in points column by team df.withColumn('maxPoints', F.max('points').over(w)) .where(F.col('points') == F.col('maxPoints')) .drop('maxPoints') .show()
This particular example returns a DataFrame that contains the rows with the max value in the points column for each unique value in the team column.
The following example shows how to use this syntax in practice.
Example: Select Row with Max Value in Each Group in PySpark
Suppose we have the following PySpark DataFrame that contains information about various basketball players:
from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() #define data data = [['A', 18, 3], ['A', 33, 5], ['A', 12, 8], ['A', 15, 10], ['B', 19, 4], ['B', 24, 4], ['B', 28, 2], ['C', 40, 7], ['C', 24, 3], ['C', 13, 4]] #define column names columns = ['team', 'points', 'assists'] #create dataframe using data and column names df = spark.createDataFrame(data, columns) #view dataframe df.show() +----+------+-------+ |team|points|assists| +----+------+-------+ | A| 18| 3| | A| 33| 5| | A| 12| 8| | A| 15| 10| | B| 19| 4| | B| 24| 4| | B| 28| 2| | C| 40| 7| | C| 24| 3| | C| 13| 4| +----+------+-------+
We can use the following syntax to return a DataFrame that contains the rows with the max value in the points column for each unique value in the team column:
from pyspark.sql import Window import pyspark.sql.functions as F #specify column to group by w = Window.partitionBy('team') #find row with max value in points column by team df.withColumn('maxPoints', F.max('points').over(w)) .where(F.col('points') == F.col('maxPoints')) .drop('maxPoints') .show() +----+------+-------+ |team|points|assists| +----+------+-------+ | A| 33| 5| | B| 28| 2| | C| 40| 7| +----+------+-------+
The resulting DataFrame contains only the rows with the max value in the points column for each unique team.
For example, the max points value among players on team A was 33.
Thus, the entire row that contained this value was included in the final DataFrame.
Additional Resources
The following tutorials explain how to perform other common tasks in PySpark: