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# PySpark DataFrame | corr method

schedule Aug 12, 2023
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PySpark DataFrame's `corr(~)` method returns the correlation of the specified numeric columns as a float.

# Parameters

1. `col1` | `string`

The first column.

2. `col2` | `string`

The second column.

3. `method` | `string` | `optional`

The type of correlation to compute. The only correlation type supported currently is the Pearson Correlation Coefficient.

# Return Value

A `float`.

# Examples

Consider the following PySpark DataFrame:

``` df = spark.createDataFrame([("Alex", 180, 80), ("Bob", 170, 70), ("Cathy", 160, 70)], ["name", "height", "weight"])df.show() +-----+------+------+| name|height|weight|+-----+------+------+| Alex| 180| 80|| Bob| 170| 70||Cathy| 160| 70|+-----+------+------+ ```

## Computing the correlation of two numeric PySpark columns

To compute the correlation between the `height` and `weight` columns:

``` df.corr("height","weight") 0.8660254037844387 ```

Here, we see that the `height` and `weight` are positively correlated with a Pearson correlation coefficient of around `0.87`.

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