## One Final Modificationedit

Just to drive the point home, let’s make one final modification to our example before moving on to new topics. Let’s add two metrics to calculate the min and max price for each make:

```GET /cars/transactions/_search?search_type=count
{
"aggs": {
"colors": {
"terms": {
"field": "color"
},
"aggs": {
"avg_price": { "avg": { "field": "price" }
},
"make" : {
"terms" : {
"field" : "make"
},
"aggs" : {
"min_price" : { "min": { "field": "price"} },
"max_price" : { "max": { "field": "price"} }
}
}
}
}
}
}```
 We need to add another `aggs` level for nesting. Then we include a `min` metric. And a `max` metric.

Which gives us the following output (again, truncated):

```{
...
"aggregations": {
"colors": {
"buckets": [
{
"key": "red",
"doc_count": 4,
"make": {
"buckets": [
{
"key": "honda",
"doc_count": 3,
"min_price": {
"value": 10000
},
"max_price": {
"value": 20000
}
},
{
"key": "bmw",
"doc_count": 1,
"min_price": {
"value": 80000
},
"max_price": {
"value": 80000
}
}
]
},
"avg_price": {
"value": 32500
}
},
...```
 The `min` and `max` metrics that we added now appear under each `make`

With those two buckets, we’ve expanded the information derived from this query to include the following:

• There are four red cars.
• The average price of a red car is \$32,500.
• Three of the red cars are made by Honda, and one is a BMW.
• The cheapest red Honda is \$10,000.
• The most expensive red Honda is \$20,000.