# ColumnCorrelation
<a name="dqdl-rule-types-ColumnCorrelation"></a>

Checks the *correlation* between two columns against a given expression. AWS Glue Data Quality uses the Pearson correlation coefficient to measure the linear correlation between two columns. The result is a number between -1 and 1 that measures the strength and direction of the relationship. 

**Syntax**

```
ColumnCorrelation <COL_1_NAME> <COL_2_NAME> <EXPRESSION>
```
+ **COL\$11\$1NAME** – The name of the first column that you want to evaluate the data quality rule against.

  **Supported column types**: Byte, Decimal, Double, Float, Integer, Long, Short
+ **COL\$12\$1NAME** – The name of the second column that you want to evaluate the data quality rule against.

  **Supported column types**: Byte, Decimal, Double, Float, Integer, Long, Short
+ **EXPRESSION** – An expression to run against the rule type response in order to produce a Boolean value. For more information, see [Expressions](dqdl.md#dqdl-syntax-rule-expressions).

**Example: Column correlation**

The following example rule checks whether the correlation coefficient between the columns `height` and `weight` has a strong positive correlation (a coefficient value greater than 0.8).

```
ColumnCorrelation "height" "weight" > 0.8
```

```
ColumnCorrelation "weightinkgs" "Salary" > 0.8 where "weightinkgs > 40"
```

 **Sample dynamic rules** 
+ `ColumnCorrelation "colA" "colB" between min(last(10)) and max(last(10))`
+ `ColumnCorrelation "colA" "colB" < avg(last(5)) + std(last(5))`

**Null behavior**

 The `ColumnCorrelation` rule will ignore rows with `NULL` values in the calculation of the correlation. For example: 

```
+---+-----------+
|id |units      |
+---+-----------+
|100|0          | 
|101|null       |
|102|20         |
|103|null       |
|104|40         |
+---+-----------+
```

 Rows 101 and 103 will be ignored, and the `ColumnCorrelation` will be 1.0.