# reduce¶

*Takes a json object, and considers each field to be a separate datapoint’s data. It then runs the transform in its argument over the elements*

`reduce`

performs a given transform on all the elements of a multi-element datapoint.

Suppose you have the following data:

```
[
{
"a": 3,
"b": 5
},
{
"q": 10,
"z": 23
}
]
```

Then the transform `reduce(sum)`

will give you:

```
[8, 33]
```

Reduce took each element in the given datapoint, and applied the transform `sum`

to it.

## Usage¶

The reduce transform is particularly useful in conjunction with the `map`

transform.

Suppose you want to find the average number of steps taken every weekday.

Running the following transform will give you a map of weekday to average step count:

```
while(day==d[1]:day,sum) | map(weekday, mean)
```

For example, a possible result of the above transform could be:

```
[
{
"Monday": 12243,
"Tuesday": 13452,
"Wednesday": 14523,
"Thursday": 9543,
"Friday": 20487,
"Saturday": 3000,
"Sunday": 4000
}
]
```

You can now find the average per weekday by running `reduce(mean)`

, giving a final transform:

```
while(day==d[1]:day,sum) | map(weekday, mean) | reduce(mean)
```

### Transform Details¶

Input Schema | Output Schema |
---|---|

```
{}
``` | ```
{}
``` |

### Arguments¶

# | Description | Type | Schema |
---|---|---|---|

1 | The transform to instantiate for each datapoint's values. | pipe | ```
{}
``` |