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Groups the DataFrame by the specified columns so that aggregation can be performed on them. See GroupedData for all the available aggregate functions.
Syntax
groupBy(*cols: "ColumnOrNameOrOrdinal")
Parameters
| Parameter | Type | Description |
|---|---|---|
cols |
list, str, int or Column | The columns to group by. Each element can be a column name (string) or an expression (Column) or a column ordinal (int, 1-based) or list of them. |
Returns
GroupedData: A GroupedData object representing the grouped data by the specified columns.
Notes
A column ordinal starts from 1, which is different from the 0-based __getitem__.
Examples
df = spark.createDataFrame([
("Alice", 2), ("Bob", 2), ("Bob", 2), ("Bob", 5)], schema=["name", "age"])
df.groupBy().avg().show()
# +--------+
# |avg(age)|
# +--------+
# | 2.75|
# +--------+
df.groupBy("name").agg({"age": "sum"}).sort("name").show()
# +-----+--------+
# | name|sum(age)|
# +-----+--------+
# |Alice| 2|
# | Bob| 9|
# +-----+--------+
df.groupBy(df.name).max().sort("name").show()
# +-----+--------+
# | name|max(age)|
# +-----+--------+
# |Alice| 2|
# | Bob| 5|
# +-----+--------+
df.groupBy(["name", df.age]).count().sort("name", "age").show()
# +-----+---+-----+
# | name|age|count|
# +-----+---+-----+
# |Alice| 2| 1|
# | Bob| 2| 2|
# | Bob| 5| 1|
# +-----+---+-----+