Prefacio:
Como ingeniero de desarrollo de pruebas, es inevitable que necesite utilizar datos para realizar pruebas durante las pruebas. A veces, es necesario generar una cierta cantidad de datos falsos en lotes para realizar pruebas. Es realmente demasiado complicado de construir por sí mismo. Hoy hablaré sobre una muy buena forma de construir el marco de datos falsos. Hay una biblioteca muy poderosa en Python llamada Faker, que puede ayudarnos automáticamente a generar una variedad de datos "falsos" de apariencia real. Aprendamos juntos sobre esta biblioteca de Faker.
Aquellos que han aprendido Python saben que no diré mucho, instale la biblioteca Faker
pip3 install fakerImportar llamada a la biblioteca de Faker
# coding:utf8
from faker import Faker
# 中文数据
faker = Faker('zh_CN')Faker construye los diversos módulos de los datos, o cada clase, cada método, el siguiente es un relativamente completo, temporalmente apilado. Básicamente, todo lo que se utiliza está cubierto.
# -*- coding: utf-8 -*-
from faker import Factory
# 产生随机手机号
print(fake.phone_number())
# 产生随机姓名
print(fake.name())
# 产生随机地址
print(fake.address())
# 随机产生国家名
print(fake.country())
# 随机产生国家代码
print(fake.country_code())
# 随机产生城市名
print(fake.city_name())
# 随机产生城市
print(fake.city())
# 随机产生省份
print(fake.province())
# 产生随机email
print(fake.email())
# 产生随机IPV4地址
print(fake.ipv4())
# 产生长度在最大值与最小值之间的随机字符串
print(fake.pystr(min_chars=0, max_chars=8))
# 随机产生车牌号
print(fake.license_plate())
# 随机产生颜色
print(fake.rgb_color()) # rgb
print(fake.safe_hex_color()) # 16进制
print(fake.color_name()) # 颜色名字
print(fake.hex_color()) # 16进制
# 随机产生公司名
print(fake.company())
# 随机产生工作岗位
print(fake.job())
# 随机生成密码
print(fake.password(length=10, special_chars=True, digits=True, upper_case=True, lower_case=True))
# 随机生成uuid
print(fake.uuid4())
# 随机生成sha1
print(fake.sha1(raw_output=False))
# 随机生成md5
print(fake.md5(raw_output=False))
# 随机生成女性名字
print(fake.name_female())
# 男性名字
print(fake.name_male())
# 随机生成名字
print(fake.name())
# 生成基本信息
print(fake.profile(fields=None, sex=None))
print(fake.simple_profile(sex=None))
# 随机生成浏览器头user_agent
print(fake.user_agent())
# 随机产生时间
fake.month_name()
# 'September'
fake.date_time_this_century(before_now=True, after_now=False, tzinfo=None)
# datetime.datetime(2010, 7, 21, 18, 52, 43)
fake.time_object(end_datetime=None)
# datetime.time(6, 39, 26)
fake.date_time_between(start_date="-30y", end_date="now", tzinfo=None)
# datetime.datetime(2013, 10, 11, 18, 43, 40)
fake.future_date(end_date="+30d", tzinfo=None)
# datetime.date(2018, 7, 8)
fake.date_time(tzinfo=None, end_datetime=None)
# datetime.datetime(2006, 9, 4, 20, 46, 6)
fake.date(pattern="%Y-%m-%d", end_datetime=None)
# '1998-08-02'
fake.date_time_this_month(before_now=True, after_now=False, tzinfo=None)
# datetime.datetime(2018, 6, 8, 9, 56, 24)
fake.timezone()
# 'Africa/Conakry'
fake.date_time_this_decade(before_now=True, after_now=False, tzinfo=None)
# datetime.datetime(2017, 6, 27, 21, 18, 28)
fake.month()
# '04'
fake.day_of_week()
# 'Wednesday'
fake.iso8601(tzinfo=None, end_datetime=None)
# '1988-02-28T09:22:29'
fake.time_delta(end_datetime=None)
# datetime.timedelta(10832, 82660)
fake.date_object(end_datetime=None)
# datetime.date(2005, 8, 18)
fake.date_this_decade(before_today=True, after_today=False)
# datetime.date(2015, 1, 5)
fake.date_this_century(before_today=True, after_today=False)
# datetime.date(2000, 6, 1)
fake.date_this_month(before_today=True, after_today=False)
# datetime.date(2018, 6, 13)
fake.am_pm()
# 'AM'
fake.past_datetime(start_date="-30d", tzinfo=None)
# datetime.datetime(2018, 6, 25, 7, 41, 34)
fake.date_this_year(before_today=True, after_today=False)
# datetime.date(2018, 2, 24)
fake.date_time_between_dates(datetime_start=None, datetime_end=None, tzinfo=None)
# datetime.datetime(2018, 6, 26, 14, 40, 5)
fake.date_time_ad(tzinfo=None, end_datetime=None)
# datetime.datetime(673, 1, 28, 18, 17, 55)
fake.date_between_dates(date_start=None, date_end=None)
# datetime.date(2018, 6, 26)
fake.future_datetime(end_date="+30d", tzinfo=None)
# datetime.datetime(2018, 7, 4, 10, 53, 6)
fake.century()
# 'IX'
fake.past_date(start_date="-30d", tzinfo=None)
# datetime.date(2018, 5, 30)
fake.time(pattern="%H:%M:%S", end_datetime=None)
# '01:32:14'
fake.day_of_month()
# '19'
fake.unix_time(end_datetime=None, start_datetime=None)
# 1284297794
fake.date_time_this_year(before_now=True, after_now=False, tzinfo=None)
# datetime.datetime(2018, 5, 24, 11, 25, 25)
fake.date_between(start_date="-30y", end_date="today")
# datetime.date(2003, 1, 11)
fake.year()
# '1993'
fake.time_series(start_date="-30d", end_date="now", precision=None, distrib=None, tzinfo=None)
# <generator object time_series at 0x7f44e702a620>
# 随机产生文件
fake.file_extension(category=None)
# 'xls'
fake.file_name(category=None, extension=None)
# '表示.csv'
fake.file_path(depth=1, category=None, extension=None)
# '/教育/客户.js'
fake.unix_device(prefix=None)
# '/dev/sdf'
fake.unix_partition(prefix=None)
# '/dev/vdf0'
fake.mime_type(category=None)
# 'multipart/form-data'El blog de este artículo hace referencia al siguiente enlace https://www.cnblogs.com/felixwang2/p/9246279.html