### 如何在列表中按多个字段进行 `group by` 操作 #### Python 使用 Pandas 进行多字段分组 对于给定的数据集，可以通过 Pandas 库中的 `groupby()` 方法来实现基于多个字段的分组。此方法允许通过指定一个或多个列名来进行复杂的数据分析。 ```python import pandas as pd data = { 'Company': ['Google', 'Facebook', 'Google', 'Microsoft'], 'Department': ['Engineering', 'HR', 'Marketing', 'Engineering'], 'Age': [30, 25, 40, 35], 'Salary': [90000, 70000, 80000, 120000] } df = pd.DataFrame(data) # 对 Company 和 Department 列进行分组，并计算每组 Age 平均值和 Salary 中位数 result = df.groupby(['Company', 'Department']).agg({'Age': 'mean', 'Salary': 'median'}) print(result) ``` 上述代码展示了如何使用 Pandas 来执行复杂的分组逻辑并应用不同的聚合函数[^2]。 #### Java 使用 Stream API 实现多字段分组 Java 8 提供了强大的流式处理能力，能够方便地完成集合类对象上的多条件分组： ```java import java.util.*; import java.util.stream.Collectors; class Employee { private String company; private String department; private int age; private double salary; public Employee(String company, String department, int age, double salary) { this.company = company; this.department = department; this.age = age; this.salary = salary; } // Getters and setters... @Override public String toString() { return "Employee{" + "company='" + company + '\'' + ", department='" + department + '\'' + '}'; } } public class Main { public static void main(String[] args) { List<Employee> employees = Arrays.asList( new Employee("Google", "Engineering", 30, 90_000), new Employee("Facebook", "HR", 25, 70_000), new Employee("Google", "Marketing", 40, 80_000), new Employee("Microsoft", "Engineering", 35, 120_000)); Map<String, Map<String, List<Employee>>> grouped = employees.stream() .collect(Collectors.groupingBy(Employee::getCompany, Collectors.groupingBy(Employee::getDepartment))); System.out.println(grouped); } } ``` 这段程序说明了怎样借助于 `Collectors.groupingBy()` 函数链式调用来达到双重分组的效果[^4]。