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

Use the `Distribution` Analyzer to compute the frequency distribution of values in a column. The analyzer produces different outputs depending on the column data type:
+ **Numeric columns** – Generates a binned histogram. The histogram divides the column's value range into equal-width intervals (bins) and counts the number of values in each bin.
+ **String, date, and other non-numeric columns** – Generates a value distribution that counts the frequency of each distinct value, sorted in descending order by frequency. The analyzer returns only the top 20 most frequent values.

**Note**  
You can use the `Distribution` Analyzer only in the `Analyzers` block of your DQDL ruleset. It cannot be used as a rule.

**Syntax**

```
Distribution {{<COL_NAME>}}
Distribution {{<COL_NAME>}} with bins = {{<BIN_COUNT>}}
Distribution {{<COL_NAME>}} with bins = {{<BIN_COUNT>}}, rebin = {{<true|false>}}
```
+ **COL\_NAME** – The name of the column to analyze, or `AllColumns` to compute distributions for all columns in the dataset.

  **Supported column types**: Any column type
+ **bins** (optional) – The number of bins (intervals) to use for numeric column histograms. The default value is 20. The maximum value is 20. This parameter has no effect on non-numeric columns.
+ **rebin** (optional) – Controls automatic rebinning behavior. When overflow exceeds the threshold on consecutive runs, the analyzer rebins automatically. The default value is `true`. Set to `false` to keep bin edges frozen indefinitely regardless of overflow frequency. For more information, see [Automatic rebinning](dqdl.md#dqdl-analyzers-distribution-rebinning).

**Example – Basic distribution for a single column**

The following example computes a frequency distribution for the `Salary` column using the default 20 bins:

```
Analyzers = [
    Distribution "Salary"
]
```

**Example – Custom bin count**

The following example computes a histogram for the `Age` column using 10 bins:

```
Analyzers = [
    Distribution "Age" with bins = 10
]
```

**Example – Distribution for all columns**

The following example computes distributions for all columns in the dataset. Numeric columns produce histograms with 20 bins, and non-numeric columns produce value distributions:

```
Analyzers = [
    Distribution AllColumns
]
```

**Example – Combining with rules and other analyzers**

The following example shows a ruleset that combines rules, standard analyzers, and the `Distribution` Analyzer:

```
Rules = [
    RowCount > 100,
    Completeness "Salary" > 0.9
]
Analyzers = [
    Mean "Salary",
    StandardDeviation "Salary",
    Distribution "Salary" with bins = 15,
    Distribution "Department"
]
```

**Output format**

When you run the `Distribution` Analyzer, it produces a statistic named `Distribution`. Scalar statistics (such as `Mean` or `Sum`) return a single numeric value. The `Distribution` statistic, by contrast, returns a structured result:
+ `BinEdges` – An array defining the bin boundaries (for numeric columns) or distinct values (for non-numeric columns).
+ `Count` – An array of frequencies for each bin or value.

You can retrieve distribution results using the `ListDataQualityStatistics` API or the `GetDataQualityResult` API. For more information about the Data Quality APIs, see [Data Quality APIs](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-api-data-quality.html) in the *AWS Glue API Reference*.

**Null behavior**

The analyzer excludes rows with NULL values in the target column from the bin calculation.

For more information about frozen bin edges, overflow observations, and automatic rebinning, see [Distribution Analyzer](dqdl.md#dqdl-analyzers-distribution).