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PySpark DataFrame - Calculate sum and avg with groupBy

visibility 951 comment 0 access_time 2 years ago language English

descriptionCode description

This code snippet provides an example of calculating aggregated values after grouping data in PySpark DataFrame. To group data, DataFrame.groupby or DataFrame.groupBy can be used; then GroupedData.agg method can be used to aggregate data for each group. Built-in aggregation functions like sum, avg, max, min and others can be used. Customized aggregation functions can also be used.

Output:

+----------+--------+
|TotalScore|AvgScore|
+----------+--------+
|       392|    78.4|
+----------+--------+

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from pyspark.sql import SparkSession
from pyspark.sql import functions as F

app_name = "PySpark sum and avg Examples"
master = "local"

spark = SparkSession.builder \
    .appName(app_name) \
    .master(master) \
    .getOrCreate()

spark.sparkContext.setLogLevel("WARN")

data = [
    [101, 56],
    [102, 78],
    [103, 70],
    [104, 93],
    [105, 95]
]

df = spark.createDataFrame(data, ['Student', 'Score'])

df_agg = df.groupBy().agg(F.sum('Score').alias(
    'TotalScore'), F.avg('Score').alias('AvgScore'))

df_agg.show()

PySpark DataFrame - Calculate sum and avg with groupBy

descriptionCode description

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