Faker로 현실적인 데이터세트 생성
Python으로 배우는 데이터 프라이버시와 익명화
Rebeca Gonzalez
Data engineer
Faker로 데이터 생성
fake_data.name()
'Kelly Clark'
fake_data.name_male()
'Antonio Henderson'
fake_data.name_female()
'Jennifer Ortega'
Clients 데이터프레임
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
Faker로 데이터세트 생성
- 성별에 맞는 고유 이름 생성
- 무작위 도시 생성
- 확률 분포를 따르는 지정 도시 생성
- 이메일 생성
- 기간 내 날짜 생성
성별과 일치하는 이름 만들기
데이터세트에서 고유한 이름 생성
중복 피하기
# 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']]
성별과 일치하는 이름 만들기
# 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
무작위 도시 생성
# 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
이메일 생성
# 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]
이메일 생성
# 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]
날짜 생성
두 시점 사이의 날짜
# 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
확률 분포에 따른 도시 생성
실제 데이터세트를 모사할 때, 실제 값의 이름이 유출되지 않도록 할 수 있습니다.
# 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)
확률 분포에 따른 도시 생성
# 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
데이터세트를 만들어 봅시다!
Python으로 배우는 데이터 프라이버시와 익명화
