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# PySpark SQL Functions | max method

schedule Aug 12, 2023
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PySpark SQL Functions' `max(~)` method returns the maximum value in the specified column.

# Parameters

1. `col` | `string` or `Column`

The column in which to obtain the maximum value.

# Return Value

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

# Examples

Consider the following PySpark DataFrame:

``` df = spark.createDataFrame([["Alex", 25], ["Bob", 30]], ["name", "age"])df.show() +----+---+|name|age|+----+---+|Alex| 25|| Bob| 30|+----+---+ ```

## Getting the maximum value of a PySpark column

To obtain the maximum `age`:

``` import pyspark.sql.functions as Fdf.select(F.max("age")).show() +--------+|max(age)|+--------+| 30|+--------+ ```

To obtain the maximum `age` as an integer:

``` list_rows = df.select(F.max("age")).collect()list_rows[0][0] 30 ```

Here, the `collect()` method returns a list of `Row` objects, which in this case is length one because the PySpark DataFrame returned by `select(~)` only has one row. The content of the `Row` object can be accessed via `[0]`.

## Getting the maximum value of each group in PySpark

Consider the following PySpark DataFrame:

``` df = spark.createDataFrame([["Alex", 20, "A"],\ ["Bob", 30, "B"],\ ["Cathy", 50, "A"]], ["name", "age", "class"])df.show() +-----+---+-----+| name|age|class|+-----+---+-----+| Alex| 20| A|| Bob| 30| B||Cathy| 50| A|+-----+---+-----+ ```

To get the maximum `age` of each `class`:

``` df.groupby("class").agg(F.max("age").alias("MAX AGE")).show() +-----+-------+|class|MAX AGE|+-----+-------+| A| 50|| B| 30|+-----+-------+ ```

Here, we are using the `alias(~)` method to assign a label to the aggregated `age` column.

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