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# PySpark SQL Functions | mean method

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

# Parameters

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

The column in which to obtain the mean 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 mean of a PySpark column

To obtain the mean `age`:

``` import pyspark.sql.functions as Fdf.select(F.mean("age")).show() +--------+|avg(age)|+--------+| 27.5|+--------+ ```

To get the mean `age` as an integer:

``` list_rows = df.select(F.mean("age")).collect()list_rows[0][0] 27.5 ```

Here, we are converting the PySpark DataFrame returned from `select(~)` into a list of `Row` objects using the `collect()` method. This list is guaranteed to be of size one because the `mean(~)` reduces column values into a single number. To access the content of the `Row` object, we use another `[0]`.

## Getting the mean 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 mean `age` of each `class`:

``` df.groupby("class").agg(F.mean("age").alias("MEAN AGE")).show() +-----+--------+|class|MEAN AGE|+-----+--------+| A| 35.0|| B| 30.0|+-----+--------+ ```

Here, we are using `alias("MEAN AGE")` to assign a label to the aggregated `age` column.

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