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# $百分位数
<a name="percentile"></a>

8.0.1 版中的新增内容。

Amazon DocumentDB 中的`$percentile`运算符为数字数据计算指定的百分位值。作为累加器，它在聚合管道`$group`阶段计算组内文档的百分位数值。作为表达式，它计算数字数组的百分位数。

**参数**
+ `input`：解析为数值或数值数组的表达式。
+ `p`：介于 0 和 1 之间的百分位数值的数组，其中每个值代表要计算的百分位数。例如，`[0.25, 0.5, 0.75]`计算第 25、50 和 75 个百分位数。
+ `method`：一个指定计算方法的字符串。目前仅支持 `"approximate"`。

## 行为
<a name="percentile-behavior"></a>

该`"approximate"`方法使用 t-digest 算法来计算基于百分位数的近似指标。对于小型数据集，不同的百分位数值可能会解析为相同的结果。例如，如果每组只有 5 个值，则 p90 和 p99 都可能返回该组中的最大值。随着数据点数量的增加，精度也会提高。

## 示例（MongoDB Shell）
<a name="percentile-examples"></a>

以下示例说明如何使用`$percentile`运算符计算每个班级分数的第 25 和第 75 个百分位数。

**创建示例文档 **

```
db.students.insertMany([
  { class: "A", score: 72 },
  { class: "A", score: 85 },
  { class: "A", score: 90 },
  { class: "A", score: 68 },
  { class: "A", score: 95 },
  { class: "B", score: 80 },
  { class: "B", score: 75 },
  { class: "B", score: 92 },
  { class: "B", score: 88 },
  { class: "B", score: 70 }
]);
```

**查询示例 **

```
db.students.aggregate([
  { $group: {
      _id: "$class",
      percentiles: { $percentile: { input: "$score", p: [0.25, 0.75], method: "approximate" } }
    }}
]);
```

**输出**

```
[
  { "_id": "A", "percentiles": [72, 90] },
  { "_id": "B", "percentiles": [75, 88] }
]
```

## 表达式用法示例（MongoDB Shell）
<a name="percentile-expression-examples"></a>

该`$percentile`运算符还可用作`$project`阶段内的表达式来计算数组字段的百分位数。

**创建示例文档 **

```
db.surveys.insertMany([
  { _id: 1, responses: [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] },
  { _id: 2, responses: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] }
]);
```

**查询示例 **

```
db.surveys.aggregate([
  { $project: {
      quartiles: { $percentile: { input: "$responses", p: [0.25, 0.5, 0.75], method: "approximate" } }
    }}
]);
```

**输出**

```
[
  { "_id": 1, "quartiles": [6, 10, 16] },
  { "_id": 2, "quartiles": [5, 9, 15] }
]
```

## 代码示例
<a name="percentile-code"></a>

要查看使用`$percentile`运算符的代码示例，请选择要使用的语言的选项卡。以下示例显示了累加器的用法（输入`$group`）和表达式用法（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: percentiles across grouped documents
    const students = db.collection('students');
    const accumulatorResult = await students.aggregate([
      { $group: {
          _id: "$class",
          percentiles: { $percentile: { input: "$score", p: [0.25, 0.75], method: "approximate" } }
        }}
    ]).toArray();
    console.log('Accumulator result:', accumulatorResult);

    // Expression usage: percentiles of an array field
    const surveys = db.collection('surveys');
    const expressionResult = await surveys.aggregate([
      { $project: {
          quartiles: { $percentile: { input: "$responses", p: [0.25, 0.5, 0.75], method: "approximate" } }
        }}
    ]).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: percentiles across grouped documents
        students = db['students']
        accumulator_result = list(students.aggregate([
            { '$group': {
                '_id': '$class',
                'percentiles': { '$percentile': { 'input': '$score', 'p': [0.25, 0.75], 'method': 'approximate' } }
            }}
        ]))
        print('Accumulator result:', accumulator_result)

        # Expression usage: percentiles of an array field
        surveys = db['surveys']
        expression_result = list(surveys.aggregate([
            { '$project': {
                'quartiles': { '$percentile': { 'input': '$responses', 'p': [0.25, 0.5, 0.75], 'method': 'approximate' } }
            }}
        ]))
        print('Expression result:', expression_result)

    finally:
        client.close()

example()
```

------