Introduction to MongoDB $stdDevPop Operator

$stdDevPop is an operator in the MongoDB aggregation framework used to calculate the population standard deviation of all numerical fields in a collection. It returns the population standard deviation of all numerical values.

Syntax

The syntax for the $stdDevPop operator is as follows:

{ $stdDevPop: <expression> }

where <expression> is a numerical expression that can be a field name, number, or any expression that returns a number.

Use Cases

The $stdDevPop operator can be used in scenarios where one needs to understand the distribution of numerical fields in a collection, such as in statistical data analysis.

Examples

Here are two examples of the $stdDevPop operator:

Example 1

Suppose we have a student grades collection that includes the name and score of each student. We can use the $stdDevPop operator to calculate the population standard deviation of all students’ scores:

db.grades.aggregate([
  {
    $group: {
      _id: null,
      stdDev: { $stdDevPop: "$score" }
    }
  }
])

The above code groups all scores together using the $group operator and applies the $stdDevPop operator to calculate the population standard deviation of all scores. The query output is as follows:

{ "_id" : null, "stdDev" : 14.82143377436973 }

Example 2

Suppose we have a sales dataset that includes the quantity and price of each product sold. We can use the $stdDevPop operator to calculate the population standard deviation of all products’ quantities:

db.sales.aggregate([
  {
    $group: {
      _id: null,
      stdDev: { $stdDevPop: "$quantity" }
    }
  }
])

The above code groups all quantities together using the $group operator and applies the $stdDevPop operator to calculate the population standard deviation of all quantities. The query output is as follows:

{ "_id" : null, "stdDev" : 9.065307024306163 }

Conclusion

The $stdDevPop operator is a powerful tool in the MongoDB aggregation framework used to calculate the population standard deviation of all numerical fields in a collection. It can help us understand the distribution of numerical fields and provide useful insights in statistical data analysis and other fields.