---
navigation_title: "Normalize"
mapped_pages:
  - https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-pipeline-normalize-aggregation.html
---

# Normalize aggregation [search-aggregations-pipeline-normalize-aggregation]


A parent pipeline aggregation which calculates the specific normalized/rescaled value for a specific bucket value. Values that cannot be normalized, will be skipped using the [skip gap policy](/reference/aggregations/pipeline.md#gap-policy).

## Syntax [_syntax_20]

A `normalize` aggregation looks like this in isolation:

```js
{
  "normalize": {
    "buckets_path": "normalized",
    "method": "percent_of_sum"
  }
}
```

$$$normalize_pipeline-params$$$

| Parameter Name | Description | Required | Default Value |
| --- | --- | --- | --- |
| `buckets_path` | The path to the buckets we wish to normalize (see [`buckets_path` syntax](/reference/aggregations/pipeline.md#buckets-path-syntax) for more details) | Required |  |
| `method` | The specific [method](#normalize_pipeline-method) to apply | Required |  |
| `format` | [DecimalFormat pattern](https://docs.oracle.com/en/java/javase/11/docs/api/java.base/java/text/DecimalFormat.html) for theoutput value. If specified, the formatted value is returned in the aggregation’s`value_as_string` property | Optional | `null` |


## Methods [_methods]

$$$normalize_pipeline-method$$$
The Normalize Aggregation supports multiple methods to transform the bucket values. Each method definition will use the following original set of bucket values as examples: `[5, 5, 10, 50, 10, 20]`.

*rescale_0_1*
:   This method rescales the data such that the minimum number is zero, and the maximum number is 1, with the rest normalized linearly in-between.

    ```
    x' = (x - min_x) / (max_x - min_x)
    ```
    ```
    [0, 0, .1111, 1, .1111, .3333]
    ```


*rescale_0_100*
:   This method rescales the data such that the minimum number is zero, and the maximum number is 100, with the rest normalized linearly in-between.

    ```
    x' = 100 * (x - min_x) / (max_x - min_x)
    ```
    ```
    [0, 0, 11.11, 100, 11.11, 33.33]
    ```


*percent_of_sum*
:   This method normalizes each value so that it represents a percentage of the total sum it attributes to.

    ```
    x' = x / sum_x
    ```
    ```
    [5%, 5%, 10%, 50%, 10%, 20%]
    ```


*mean*
:   This method normalizes such that each value is normalized by how much it differs from the average.

    ```
    x' = (x - mean_x) / (max_x - min_x)
    ```
    ```
    [4.63, 4.63, 9.63, 49.63, 9.63, 9.63, 19.63]
    ```


*z-score*
:   This method normalizes such that each value represents how far it is from the mean relative to the standard deviation

    ```
    x' = (x - mean_x) / stdev_x
    ```
    ```
    [-0.68, -0.68, -0.39, 1.94, -0.39, 0.19]
    ```


*softmax*
:   This method normalizes such that each value is exponentiated and relative to the sum of the exponents of the original values.

    ```
    x' = e^x / sum_e_x
    ```
    ```
    [2.862E-20, 2.862E-20, 4.248E-18, 0.999, 9.357E-14, 4.248E-18]
    ```



## Example [_example_8]

The following snippet calculates the percent of total sales for each month:

```console
POST /sales/_search
{
  "size": 0,
  "aggs": {
    "sales_per_month": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "price"
          }
        },
        "percent_of_total_sales": {
          "normalize": {
            "buckets_path": "sales",          <1>
            "method": "percent_of_sum",       <2>
            "format": "00.00%"                <3>
          }
        }
      }
    }
  }
}
```

1. `buckets_path` instructs this normalize aggregation to use the output of the `sales` aggregation for rescaling
2. `method` sets which rescaling to apply. In this case, `percent_of_sum` will calculate the sales value as a percent of all sales in the parent bucket
3. `format` influences how to format the metric as a string using Java’s `DecimalFormat` pattern. In this case, multiplying by 100 and adding a *%*


And the following may be the response:

```console-result
{
   "took": 11,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "sales_per_month": {
         "buckets": [
            {
               "key_as_string": "2015/01/01 00:00:00",
               "key": 1420070400000,
               "doc_count": 3,
               "sales": {
                  "value": 550.0
               },
               "percent_of_total_sales": {
                  "value": 0.5583756345177665,
                  "value_as_string": "55.84%"
               }
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "sales": {
                  "value": 60.0
               },
               "percent_of_total_sales": {
                  "value": 0.06091370558375635,
                  "value_as_string": "06.09%"
               }
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2,
               "sales": {
                  "value": 375.0
               },
               "percent_of_total_sales": {
                  "value": 0.38071065989847713,
                  "value_as_string": "38.07%"
               }
            }
         ]
      }
   }
}
```