pyspark.sql.DataFrame.groupBy

DataFrame.groupBy(*cols)

Groups the DataFrame using the specified columns, so we can run aggregation on them. See GroupedData for all the available aggregate functions.

groupby() is an alias for groupBy().

New in version 1.3.0.

Parameters:

colslist, str or Column

columns to group by. Each element should be a column name (string) or an expression (Column).

Examples

>>> df.groupBy().avg().collect()
[Row(avg(age)=3.5)]
>>> sorted(df.groupBy('name').agg({'age': 'mean'}).collect())
[Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)]
>>> sorted(df.groupBy(df.name).avg().collect())
[Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)]
>>> sorted(df.groupBy(['name', df.age]).count().collect())
[Row(name='Alice', age=2, count=1), Row(name='Bob', age=5, count=1)]

pyspark.sql.DataFrame.freqItems pyspark.sql.DataFrame.head