分类数据匿名化

Python 中的数据隐私与匿名化

Rebeca Gonzalez

Instructor

泛化

    Age    Gender    Department    Condition
0    30    F         Finance       Anxiety disorders
1    42    M         Production    Bronchitis
2    35    F         Marketing     Dysthymia
3    39    F         Production    Dysthymia
4    40    M         Marketing     Flu

    Age    Gender    Department    Condition
0    <40   F         Finance       Anxiety disorders
1    >=40  M         Production    Bronquitis
2    <40   F         Finance       Dysthymia
3    <40   F         Production    Dysthymia
4    >=40  M         Marketing     Flu

分类数据的泛化

# 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

分类数据

可能值有限或固定。

种族
性别
家乡
年龄组
教育水平
喜欢的电影及偏好

分类数据匿名化

Education Field 中分类值的直方图

分类数据匿名化

     Department                EducationField
0    Sales                     Life Sciences
1    Research & Development    Life Sciences
2    Research & Development    Other
3    Research & Development    Life Sciences
4    Research & Development    Medical

原始数据集

     Department                EducationField
0    Sales                     Medical
1    Research & Development    Marketing
2    Research & Development    Life Sciences
3    Research & Development    Other
4    Research & Development    Life Sciences

在原始数据集中按 educationField 列的概率分布进行抽样后的结果数据集。

从数据中抽样

美国人口普查局公开发布其收集的公民数据样本。

用于计算大规模统计模式：

均值
方差
聚类

探索分布

# 显示各唯一值的绝对频数
hr['EducationField'].value_counts()

Life Sciences       606
Medical             464
Marketing           159
Technical Degree    132
Other                82
Human Resources      27
Name: EducationField, dtype: int64

探索分布

# 生成分类条形图
df['BusinessTravel'].value_counts().plot(kind='bar')

Business Travel 列中分类值的条形图

探索分布

# 获取各唯一值的绝对频数
counts = hr['EducationField'].value_counts()

# 打印索引列表
print(counts.index)

Index(['Life Sciences', 'Medical', 'Marketing', 
      'Technical Degree', 'Other', 'Human Resources'],
       dtype='object')

探索分布

# 各唯一值的概率分布
counts = df['EducationField'].value_counts(normalize=True)

Life Sciences       0.412245
Medical             0.315646
Marketing           0.108163
Technical Degree    0.089796
Other               0.055782
Human Resources     0.018367
Name: EducationField, dtype: float64

探索分布

# 各唯一值频率的取值
df['EducationField'].value_counts(normalize=True).values

array([0.4122449 , 0.31564626, 0.10816327, 0.08979592, 0.05578231,
       0.01836735])

从相同分布中抽样

# 按概率分布进行抽样
hr_sample['EducationField']= np.random.choice(counts.index, 
                                              p=counts.values, 
                                              size=len(hr))


# 查看结果数据集
hr.head()

    Age    BusinessTravel        Department                EducationField    EmployeeNumber
0    41    Travel_Rarely         Sales                     Life Sciences     1
1    49    Travel_Frequently     Research & Development    Medical           2
2    37    Travel_Rarely         Research & Development    Marketing         4
3    33    Travel_Frequently     Research & Development    Technical Degree  5
4    27    Travel_Rarely         Research & Development    Medical           7

从相同分布中抽样

# 显示各类别的绝对频数
hr['EducationField'].value_counts()

Life Sciences       606
Medical             464
Marketing           159
Technical Degree    132
Other                82
Human Resources      27
Name: EducationField, dtype: int64

# 显示结果列的频数
hr_sample['EducationField'].value_counts()

Life Sciences       604
Medical             493
Marketing           158
Technical Degree    120
Other                61
Human Resources      34
Name: EducationField, dtype: int64

Ayo berlatih!

Python 中的数据隐私与匿名化