# $stdDevPop
<a name="stdDevPop"></a>

New from version 8.0.1.

The `$stdDevPop` operator in Amazon DocumentDB calculates the population standard deviation of numeric values. As an accumulator, it computes the population standard deviation across documents within a group in the `$group` stage of an aggregation pipeline. As an expression, it calculates the population standard deviation of an array of numbers. The population standard deviation uses N as the divisor (not N-1). Non-numeric values are ignored. If there are no numeric values, it returns `null`. If there is only one numeric value, it returns `0`.

**Parameters**
+ `expression`: An expression that resolves to a numeric value or an array of numeric values.

## Example (MongoDB Shell)
<a name="stdDevPop-examples"></a>

The following example shows how to use the `$stdDevPop` operator to calculate the population standard deviation of scores per subject.

**Create sample documents**

```
db.scores.insertMany([
  { subject: "math", score: 60 },
  { subject: "math", score: 75 },
  { subject: "math", score: 85 },
  { subject: "math", score: 92 },
  { subject: "math", score: 78 },
  { subject: "science", score: 55 },
  { subject: "science", score: 70 },
  { subject: "science", score: 82 },
  { subject: "science", score: 91 },
  { subject: "science", score: 67 }
]);
```

**Query example**

```
db.scores.aggregate([
  { $group: {
      _id: "$subject",
      stdDev: { $stdDevPop: "$score" }
    }}
]);
```

**Output**

```
[
  { "_id": "math", "stdDev": 10.75174404457249 },
  { "_id": "science", "stdDev": 12.441864811996632 }
]
```

## Expression usage example (MongoDB Shell)
<a name="stdDevPop-expression-examples"></a>

The `$stdDevPop` operator can also be used as an expression within a `$project` stage to compute the population standard deviation of an array field.

**Create sample documents**

```
db.experiments.insertMany([
  { _id: 1, measurements: [10, 12, 14, 16, 18] },
  { _id: 2, measurements: [5, 5, 5, 5, 5] },
  { _id: 3, measurements: [2, 4, 6, 8, 10] }
]);
```

**Query example**

```
db.experiments.aggregate([
  { $project: {
      stdDev: { $stdDevPop: "$measurements" }
    }}
]);
```

**Output**

```
[
  { "_id": 1, "stdDev": 2.8284271247461903 },
  { "_id": 2, "stdDev": 0 },
  { "_id": 3, "stdDev": 2.8284271247461903 }
]
```

## Code examples
<a name="stdDevPop-code"></a>

To view a code example for using the `$stdDevPop` operator, choose the tab for the language that you want to use. The following examples show both accumulator usage (in `$group`) and expression usage (in `$project`):

------
#### [ Node.js ]

```
const { MongoClient } = require('mongodb');

async function example() {
  const uri = 'mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false';
  const client = new MongoClient(uri);

  try {
    await client.connect();
    const db = client.db('test');

    // Accumulator usage: stdDevPop across grouped documents
    const scores = db.collection('scores');
    const accumulatorResult = await scores.aggregate([
      { $group: {
          _id: "$subject",
          stdDev: { $stdDevPop: "$score" }
        }}
    ]).toArray();
    console.log('Accumulator result:', accumulatorResult);

    // Expression usage: stdDevPop of an array field
    const experiments = db.collection('experiments');
    const expressionResult = await experiments.aggregate([
      { $project: {
          stdDev: { $stdDevPop: "$measurements" }
        }}
    ]).toArray();
    console.log('Expression result:', expressionResult);

  } finally {
    await client.close();
  }
}

example();
```

------
#### [ Python ]

```
from pymongo import MongoClient

def example():
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')

    try:
        db = client['test']

        # Accumulator usage: stdDevPop across grouped documents
        scores = db['scores']
        accumulator_result = list(scores.aggregate([
            { '$group': {
                '_id': '$subject',
                'stdDev': { '$stdDevPop': '$score' }
            }}
        ]))
        print('Accumulator result:', accumulator_result)

        # Expression usage: stdDevPop of an array field
        experiments = db['experiments']
        expression_result = list(experiments.aggregate([
            { '$project': {
                'stdDev': { '$stdDevPop': '$measurements' }
            }}
        ]))
        print('Expression result:', expression_result)

    finally:
        client.close()

example()
```

------