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# PySpark SQL Functions | greatest method

schedule Aug 12, 2023
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PySpark SQL Functions' `greatest(~)` method returns the maximum value of each row in the specified columns. Note that you must specify two or more columns.

# Parameters

1. `*cols` | `string` or `Column`

The columns from which to compute the maximum values.

# Return Value

A PySpark Column (`pyspark.sql.column.Column`).

# Examples

Consider the following PySpark DataFrame:

``` df = spark.createDataFrame([["Alex", 100, 200], ["Bob", 150, 50]], ["name", "salary", "bonus"])df.show() +----+------+-----+|name|salary|bonus|+----+------+-----+|Alex| 100| 200|| Bob| 150| 50|+----+------+-----+ ```

## Getting the largest value of each row in PySpark DataFrame

To get the largest value of each row in the columns `salary` and `bonus`:

``` import pyspark.sql.functions as Fdf.select(F.greatest("salary", "bonus")).show() +-----------------------+|greatest(salary, bonus)|+-----------------------+| 200|| 150|+-----------------------+ ```

To append this column to the existing PySpark DataFrame, use the `withColumn(~)` method:

``` import pyspark.sql.functions as Fdf.withColumn("my_max", F.greatest("salary", "bonus")).show() +----+------+-----+------+|name|salary|bonus|my_max|+----+------+-----+------+|Alex| 100| 200| 200|| Bob| 150| 50| 150|+----+------+-----+------+ ```
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