有没有更好的方法来创建一个类，该类生成人口的随机性别，并根据n分配给社区？

Question

Python类，该类生成人口的随机性别（M/F），并基于n将其分配给社区

1. 人口数量=30

2. 示例人口计数为30（成人、老年人、儿童）

3. 年龄分布成人50%，儿童30%，年龄20%

4. 社区数据

      Community_num    Community_name  Community_polygon   Community_population
      301              new york        Mutipolgon([])      10
      303              JFK             Mutipolgon([])      10
      304              River side      Mutipolgon([])      10
   

预料之外

 Community_num  Community_name  Community_polygon   Community_population Gender Age  Age_Group 

  301               new york        Mutipolgon([])      10                F      27 Adult
  301               new york        Mutipolgon([])      10                M      27 Adult
  301               new york        Mutipolgon([])      10                M      10 Kids
  301               new york        Mutipolgon([])      10                M      50 Old

代码尝试

def GeneratePeople(time, n):
   
    people = [None] * n
   
    for i in range(0,n):
       
        people[i] = Person(Gender.Male, 30, time)
       
       
    return people

我有基本的，但我需要帮助

年龄

kids - below 15
adult - above 15 to 45
old  - 45 above

Answer 1

有一种方法可以做到这一点：

df["dummy"] = df.Community_population.map(lambda x: list(range(x)))
df = df.explode("dummy")
df["Gender"] = np.random.choice(["M", "F"], len(df))

df["Age_Group"] = np.random.choice(["child", "adult", "old"], len(df), p=[.3, .5, .2])
df.loc[df.Age_Group == "old", "age"] = np.random.randint(50, 90, len(df[df.Age_Group == "old"] ))

df.loc[df.Age_Group == "child", "age"] = np.random.randint(0, 18, len(df[df.Age_Group == "child"] ))
df.loc[df.Age_Group == "adult", "age"] = np.random.randint(18, 50, len(df[df.Age_Group == "adult"] ))

结果: