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$stdDevPop

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

Example (MongoDB Shell)

The following example demonstrates the use of 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)

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

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()