agg (GroupedData)

Computes aggregates and returns the result as a DataFrame.

The available aggregate functions can be:

1. Built-in aggregation functions, such as avg, max, min, sum, count.
2. Group aggregate pandas UDFs, created with pyspark.sql.functions.pandas_udf.

Syntax

agg(*exprs)

Parameters

Returns

DataFrame

Notes

Built-in aggregation functions and group aggregate pandas UDFs cannot be mixed in a single call to this function.

When exprs is a single dict, the key is the column to perform aggregation on and the value is the aggregate function. When exprs is a list of Column expressions, each expression specifies an aggregation to compute.

Examples

import pandas as pd
from pyspark.sql import functions as sf

df = spark.createDataFrame(
    [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])

# Group-by name, and count each group.
df.groupBy(df.name).agg({"*": "count"}).sort("name").show()
# +-----+--------+
# | name|count(1)|
# +-----+--------+
# |Alice|       2|
# |  Bob|       2|
# +-----+--------+

# Group-by name, and calculate the minimum age.
df.groupBy(df.name).agg(sf.min(df.age)).sort("name").show()
# +-----+--------+
# | name|min(age)|
# +-----+--------+
# |Alice|       2|
# |  Bob|       5|
# +-----+--------+

# Same as above but uses a pandas UDF.
from pyspark.sql.functions import pandas_udf

@pandas_udf('int')
def min_udf(v: pd.Series) -> int:
    return v.min()

df.groupBy(df.name).agg(min_udf(df.age)).sort("name").show()
# +-----+------------+
# | name|min_udf(age)|
# +-----+------------+
# |Alice|           2|
# |  Bob|           5|
# +-----+------------+