用 Faker 產生擬真資料集
Data Privacy and Anonymization in Python
Rebeca Gonzalez
Data engineer
用 Faker 產生資料
fake_data.name()
'Kelly Clark'
fake_data.name_male()
'Antonio Henderson'
fake_data.name_female()
'Jennifer Ortega'
Clients 的 DataFrame
clients_df
gender active
0 Female No
1 Male Yes
2 Male No
3 Female Yes
4 Male Yes
... ... ...
1465 Male Yes
1466 Male Yes
1467 Male Yes
1468 Male Yes
1469 Male Yes
1470 rows × 2 columns
用 Faker 建立資料集
- 依性別產生唯一姓名
- 產生隨機城市
- 依機率分佈產生指定城市
- 產生電子郵件
- 產生時間區間內的日期
讓姓名與性別一致
在資料集中產生唯一姓名
避免重複
# Import the Faker class from faker import Faker# Initialize a Faker class fake_data = Faker()# Generate a name according to the gender, that will be unique in the dataset clients_df['name'] = [fake_data.unique.name_female() if x == "Female"else fake_data.unique.name_male()for x in clients_df['gender']]
讓姓名與性別一致
# Explore the dataset
clients_df
gender active name
0 Female No Michelle Lang
1 Male Yes Robert Norton
2 Male No Matthew Brown
3 Female Yes Sherry Jones
4 Male Yes Steven Vega
... ... ... ...
1465 Male Yes Bradley Smith
1466 Male Yes Tyler Yu
1467 Male Yes Mr. Joshua Gallegos
1468 Male Yes Brian Aguilar
1469 Male Yes David Johnson
1470 rows × 3 columns
產生隨機城市
# Generating a random city clients_df['city'] = [fake_data.city() for x in range(len(clients_df))]clients_df.head()
Gender Active Name City
0 Female No Stacy Hooper Reedland
1 Male Yes Michael Rogers North Michellestad
2 Male No James Sanchez West Josephburgh
3 Female Yes Taylor Berger Hermanton
4 Male Yes Joshua Coleman South Amandaland
產生電子郵件
# Generating emails with different domains clients_df['contact email'] = [fake_data.company_email() for x in range(len(clients_df))]# Explore the dataset clients_df.head()
gender active name city contact email
0 Female No Stacy Hooper Reedland [email protected]
1 Male Yes Michael Rogers North Michellestad [email protected]
2 Male No James Sanchez West Josephburgh [email protected]
3 Female Yes Taylor Berger Hermanton [email protected]
4 Male Yes Joshua Coleman South Amandaland [email protected]
產生電子郵件
# Generating emails with company like domains and username similar to name clients_df['Contact email'] = [x.replace(" ", "") + "@" + fake_data.domain_name() for x in clients_df['Name']]# Explore the resulting DataFrame clients_df.head()
gender active name city contact email
0 Female No Stacy Hooper Reedland [email protected]
1 Male Yes Michael Rogers North Michellestad [email protected]
2 Male No James Sanchez West Josephburgh [email protected]
3 Female Yes Taylor Berger Hermanton [email protected]
4 Male Yes Joshua Coleman South Amandaland [email protected]
產生日期
兩個時間點之間的日期
# Generating dates between two times clients_df['date'] = [fake_data.date_between(start_date="-10y", end_date="now") for x in range(len(clients_df))]# Explore the resulting DataFrame clients_df.head()
gender active name city contact email date
0 Female No Stacy Hooper Reedland [email protected] 2019-11-20
1 Male Yes Michael Rogers North Michellestad [email protected] 2015-02-22
2 Male No James Sanchez West Josephburgh [email protected] 2015-12-11
3 Female Yes Taylor Berger Hermanton [email protected] 2012-12-13
4 Male Yes Joshua Coleman South Amandaland [email protected] 2014-05-22
依機率分佈產生城市
在模擬真實資料集時，可以避免洩漏真實值的名稱。
# Import numpy import numpy as np# Obtain or specify the probabilities p = (0.58, 0.23, 0.16, 0.03) cities = ["New York", "Chicago", "Seattle", "Dallas"]# Generate the cities from the selected ones following a distribution clients_df['city'] = np.random.choice(cities, size=len(clients_df), p=p)
依機率分佈產生城市
# See the resulting dataset
clients_df.head()
gender active name city contact email date
0 Female No Stacy Hooper Chicago [email protected] 2019-11-20
1 Male Yes Michael Rogers New York [email protected] 2015-02-22
2 Male No James Sanchez New York [email protected] 2015-12-11
3 Female Yes Taylor Berger Chicago [email protected] 2012-12-13
4 Male Yes Joshua Coleman New York [email protected] 2014-05-22
一起來練習吧！
Data Privacy and Anonymization in Python
