# Normalize aggregation

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## Normalize aggregationedit

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.

### Syntaxedit

A `normalize` aggregation looks like this in isolation:

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

Table 71. `normalize_pipeline` Parameters

Parameter Name Description Required Default Value

`buckets_path`

The path to the buckets we wish to normalize (see `buckets_path` syntax for more details)

Required

`method`

The specific method to apply

Required

`format`

format to apply to the output value of this aggregation

Optional

`null`

### Methodsedit

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]`
zscore

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]`

### Exampleedit

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

```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",
"method": "percent_of_sum",
"format": "00.00%"
}
}
}
}
}
}```
 `buckets_path` instructs this normalize aggregation to use the output of the `sales` aggregation for rescaling `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 `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:

```{
"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%"
}
}
]
}
}
}```