本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

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

8.0.1 版的新功能。

Amazon DocumentDB 中的`$stdDevSamp`運算子會計算數值的範例標準差。作為累積器，它會計算彙總管道`$group`階段中群組內文件之間的範例標準差。做為表達式，它會計算數字陣列的樣本標準差。範例標準差使用 N-1 做為除數 (Bessel 的校正）。會忽略非數值。如果少於兩個數值，則會傳回 `null`。

**參數**
+ `expression`：解析為數值或數值陣列的表達式。

## 範例 (MongoDB Shell)
<a name="stdDevSamp-examples"></a>

下列範例示範如何使用 `$stdDevSamp` 運算子來計算每個主題的分數範例標準差。

**建立範例文件**

```
db.scores.insertMany([
  { subject: "math", score: 80 },
  { subject: "math", score: 90 },
  { subject: "math", score: 85 },
  { subject: "math", score: 95 },
  { subject: "science", score: 70 },
  { subject: "science", score: 75 },
  { subject: "science", score: 80 },
  { subject: "science", score: 85 }
]);
```

**查詢範例**

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

**輸出**

```
[
  { "_id": "math", "stdDev": 6.454972243679028 },
  { "_id": "science", "stdDev": 6.454972243679028 }
]
```

## 表達式用量範例 (MongoDB Shell)
<a name="stdDevSamp-expression-examples"></a>

運算`$stdDevSamp`子也可以用作`$project`階段內的表達式，以計算陣列欄位的範例標準差。

**建立範例文件**

```
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] }
]);
```

**查詢範例**

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

**輸出**

```
[
  { "_id": 1, "stdDev": 3.1622776601683795 },
  { "_id": 2, "stdDev": 0 },
  { "_id": 3, "stdDev": 3.1622776601683795 }
]
```

## 程式碼範例
<a name="stdDevSamp-code"></a>

若要檢視使用 `$stdDevSamp` 運算子的程式碼範例，請選擇您要使用的語言標籤。下列範例顯示累積器用量 （在 中`$group`) 和表達式用量 （在 中`$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: stdDevSamp across grouped documents
    const scores = db.collection('scores');
    const accumulatorResult = await scores.aggregate([
      { $group: {
          _id: "$subject",
          stdDev: { $stdDevSamp: "$score" }
        }}
    ]).toArray();
    console.log('Accumulator result:', accumulatorResult);

    // Expression usage: stdDevSamp of an array field
    const experiments = db.collection('experiments');
    const expressionResult = await experiments.aggregate([
      { $project: {
          stdDev: { $stdDevSamp: "$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: stdDevSamp across grouped documents
        scores = db['scores']
        accumulator_result = list(scores.aggregate([
            { '$group': {
                '_id': '$subject',
                'stdDev': { '$stdDevSamp': '$score' }
            }}
        ]))
        print('Accumulator result:', accumulator_result)

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

    finally:
        client.close()

example()
```

------