匿名化连续数据
Python 中的数据隐私与匿名化
Rebeca Gonzalez
Instructor
连续变量
- 年龄
- 身高
- 体重
- 温度
- 日期和时间
连续变量
# 查看数据集
hr.head()
Age BusinessTravel Department EducationField EmployeeNumber
0 41 Travel_Rarely Sales Life Sciences 1
1 49 Travel_Frequently Research & Development Life Sciences 2
2 37 Travel_Rarely Research & Development Other 4
3 33 Travel_Frequently Research & Development Life Sciences 5
4 27 Travel_Rarely Research & Development Medical 7
连续分布
为数据选择最优连续分布。
- 绘制直方图
- 尝试连续分布，并拟合到直方图
- 选取与直方图误差最小的分布
连续分布
应用分布
import scipy.stats# 将广义逻辑斯蒂分布拟合到连续变量 Age params = scipy.stats.genlogistic.fit(hr['Age'])# 查看连续分布的参数 print(params)
(4.9899067653418285, 22.32808853181744, 7.046590524738551)
从连续分布采样
# 从广义逻辑斯蒂分布采样 df['Age'] = scipy.stats.genlogistic.rvs(size=len(df.index), *params)# 查看结果数据集 df['Age'].head()
Age BusinessTravel Department EducationField EmployeeNumber
0 40.767259 Travel_Rarely Sales Life Sciences 1
1 45.730504 Travel_Frequently Research & Development Life Sciences 2
2 41.910050 Travel_Rarely Research & Development Other 4
3 35.275320 Travel_Frequently Research & Development Life Sciences 5
4 40.198134 Travel_Rarely Research & Development Medical 7
从连续分布采样
# 四舍五入以得到离散值
df['Age'] = df['Age'].round()
Age BusinessTravel Department EducationField EmployeeNumber
0 41 Travel_Rarely Sales Life Sciences 1
1 46 Travel_Frequently Research & Development Life Sciences 2
2 42 Travel_Rarely Research & Development Other 4
3 35 Travel_Frequently Research & Development Life Sciences 5
4 40 Travel_Rarely Research & Development Medical 7
Passons à la pratique !
Python 中的数据隐私与匿名化
