python中强大的testdata库自动生成测试所需要的数据,,testdata是用


testdata是用于生成测试数据的一个安装包,它不仅提供DictFactory类来生成数据,还提供特定的扩展功能。每个Factory实例均可用于生成用户所需要的特定个数的数据,这将使我们更好地统计分析结果

如果你的目的是生成数据并将其存储到数据库或基于数据库的文档,DictFactory将会变得更加快捷

首先安装testdata,我用的是python2.7版本,在用

pip install python-testdata

遇到下面的报错信息

Command "python setup.py egg_info" failed with error code 1 in c:\users\admini~1\appdata\local\temp\pip-build-k1cjcx\fake-factory\

如下是解决方法:先安装fake-factory

pip install fake-factory

接着安装

pip install --no-deps python-testdata

然后运行下面的程序,由于我所装的版本统一用name替代了firstName和 lastName,在原文的基础下进行下改动

import testdataimport random
class Users(testdata.DictFactory):
id = testdata.CountingFactory(10)
name = testdata.FakeDataFactory(‘name‘)
address = testdata.FakeDataFactory(‘address‘)
age = testdata.RandomInteger(10, 30)
gender = testdata.RandomSelection([‘female‘, ‘male‘])
for user in Users().generate(10): # let say we only want 10 users print user

出现如下的错误提醒:

raise ImportError(error)ImportError: The ``fake-factory`` package is now called ``Faker``.

继续解决报错,原来是fake-factory的安装版本不对,重新安装并指定安装版本

pip install fake-factory==0.7.4

接下来就可以见证生成的数据啦

{‘gender‘: ‘male‘, ‘age‘: 10, ‘id‘: 10, ‘address‘: u‘09953 Sweeney Springs\nJenniferside, FM 00653-9018‘}{‘gender‘: ‘female‘, ‘age‘: 14, ‘id‘: 11, ‘address‘: u‘59348 Mike Trail Apt. 106\nNorth Scotthaven, DE 04666‘}{‘gender‘: ‘male‘, ‘age‘: 28, ‘id‘: 12, ‘address‘: u‘53128 Archer Island Suite 256\nSouth Michaelfurt, MI 83350‘}{‘gender‘: ‘male‘, ‘age‘: 18, ‘id‘: 13, ‘address‘: u‘3559 Kimberly Wall Apt. 924\nJustinfort, FM 41543-5666‘}{‘gender‘: ‘male‘, ‘age‘: 14, ‘id‘: 14, ‘address‘: u‘54451 Fisher Mount\nNorth Michelle, UT 50280-8984‘}{‘gender‘: ‘male‘, ‘age‘: 22, ‘id‘: 15, ‘address‘: u‘PSC 4666, Box 4608\nAPO AE 90517-5117‘}{‘gender‘: ‘male‘, ‘age‘: 12, ‘id‘: 16, ‘address‘: u‘6186 Lisa Groves Apt. 593\nStephenberg, TN 15302-7752‘}{‘gender‘: ‘female‘, ‘age‘: 19, ‘id‘: 17, ‘address‘: u‘3919 White Drives\nNorth Calvinport, MD 26577-9590‘}{‘gender‘: ‘female‘, ‘age‘: 30, ‘id‘: 18, ‘address‘: u‘148 Anthony Mountain\nStevenfort, RI 38933-9947‘}{‘gender‘: ‘female‘, ‘age‘: 27, ‘id‘: 19, ‘address‘: u‘2496 Michael Groves Apt. 021\nWest Kathleenside, PR 42583-3035‘}

我们也可以构造一个数据依赖于另一个数据的情况,如下

import testdata

class ExampleFactory(testdata.DictFactory): a = testdata.CountingFactory(10) b = testdata.ClonedField("a") # b will have the same value as field ‘a‘for e in ExampleFactory().generate(5): print e

此时生成的数据为

{‘a‘: 10, ‘b‘: 10}{‘a‘: 11, ‘b‘: 11}{‘a‘: 12, ‘b‘: 12}{‘a‘: 13, ‘b‘: 13}{‘a‘: 14, ‘b‘: 14}

若我们的需求是生成事件数据,比如我们是构造一个事件的开始和结束时间,并且结束时间要在开始时间的20分钟后结束,并且,我们希望各个数据的开始时间间隔12分钟

import testdataimport datetimeEVENT_TYPES = ["USER_DISCONNECT", "USER_CONNECTED", "USER_LOGIN", "USER_LOGOUT"]class EventsFactory(testdata.DictFactory):    start_time = testdata.DateIntervalFactory(datetime.datetime.now(), datetime.timedelta(minutes=12))    end_time = testdata.RelativeToDatetimeField("start_time", datetime.timedelta(minutes=20))    event_code = testdata.RandomSelection(EVENT_TYPES)for event in EventsFactory().generate(5):    print event

若要生成百分比不同的数据,比如我们想要构造工作的数据,包含用户名、状态、描述,其中状态为pending的占90%,剩下的10%的状态为‘error’。除此之外,如果状态为error的用户该为‘support’,状态为pending的用户应为‘admin’

class Job(testdata.DictFactory):    state = testdata.StatisticalValuesFactory([(‘pending‘, 90), (‘error‘, 10)])    assigned_user = testdata.ConditionalValueField(‘state‘, {‘error‘: ‘support‘}, ‘admin‘)    description = testdata.RandomLengthStringFactory()for i in Job().generate(10):    print i

生成的数据为

{‘state‘: ‘pending‘, ‘assigned_user‘: ‘admin‘, ‘description‘: ‘HKCVoGAOJZVKYqGktTakWqewxScyUSSGcMj‘}{‘state‘: ‘pending‘, ‘assigned_user‘: ‘admin‘, ‘description‘: ‘dyyLbtxfoqotlaNfWieoVvXFlzRYNOajYFVmwtXDRdVoQItDnjgptpEiBJHBgCuzqOZVwsxyWbByrJvgiTKKNyuiSsKO‘}{‘state‘: ‘error‘, ‘assigned_user‘: ‘support‘, ‘description‘: ‘nuvRQyhcvvXWJuXhCbWVUyWAmKoioTcYIBHtcPwvgRytCsPlWEvSHRFjXDUAIgPblhhHFTKzCmmitBErHzpXLBoI‘}{‘state‘: ‘pending‘, ‘assigned_user‘: ‘admin‘, ‘description‘: ‘IhrQoAkFNwqfZSxfkwCSmaGRFodFZYVHCegEnAMpTBtqUZMgaFGlAaznzUNbDrdgPDHNrAvJEQZRDUQxdDKsLvXJiMDR‘}{‘state‘: ‘pending‘, ‘assigned_user‘: ‘admin‘, ‘description‘: ‘ftINruSFdOeAqOuDyInNgIrQPoegOwlqWSFIHYNVY‘}

另外,可以对字段进行重写和新增字段操作,在前面生成用户信息的例子中,我们可以将用户的名称设置为定值,并且新建email字段

for user in Users(name=testdata.Constant(‘John‘), age=testdata.RandomInteger(40, 60), email=testdata.FakeDataFactory(‘email‘)).generate(10): # let say we only want 10 users    print user

生成的数据为

{‘name‘: ‘John‘, ‘gender‘: ‘male‘, ‘age‘: 40, ‘email‘: u‘brownamy@yahoo.com‘, ‘address‘: u‘813 Donald Route\nSouth Andrea, SC 99925‘, ‘id‘: 15}{‘name‘: ‘John‘, ‘gender‘: ‘female‘, ‘age‘: 58, ‘email‘: u‘gloriapatel@may-mitchell.com‘, ‘address‘: u‘73919 Hodges Courts\nTammyside, ME 89926-3945‘, ‘id‘: 16}{‘name‘: ‘John‘, ‘gender‘: ‘male‘, ‘age‘: 45, ‘email‘: u‘ashley40@cooper.info‘, ‘address‘: u‘95546 Mary Flats\nWest Codystad, PA 34744‘, ‘id‘: 17}{‘name‘: ‘John‘, ‘gender‘: ‘female‘, ‘age‘: 43, ‘email‘: u‘xwhite@pierce-sutton.com‘, ‘address‘: u‘4051 Reese Mission Suite 829\nNew Danny, NE 92155-8318‘, ‘id‘: 18}{‘name‘: ‘John‘, ‘gender‘: ‘male‘, ‘age‘: 60, ‘email‘: u‘qpoole@rice.com‘, ‘address‘: u‘3584 Bright Ramp\nJohnstonmouth, NV 12566-4343‘, ‘id‘: 19}

上面例子中所用的基类如下

Factory类简介
Factory  所有的Factory的基类
DictFactory非常强大的基类,它可让子类创建Factory,具体可参考上面的例子
ListFactoryA factory that returns on each iteration a list ofelements_per_listitems returned from calls to the given factory.
CallableGets a callable object as an argument and returns the result of calling the object on every iteration
DependentCallableGets a callable object as an argument and returns the result of calling the object passing the defined fields as arguments on every iteration
ClonedFieldA factory that copies the value of another factory.

日期类

Factory类简介
RandomDateFactoryGenerates random dates (python‘s datetime) between 2 dates
DateIntervalFactoryGenerates datetime objects starting frombasewhile addingdeltato it each iteration.
RelativeToDatetimeFieldGenerates datetime object relative to another datetime field, like if you havestart_timewhich is a RandomDateFactory field, and want anend_timefield that is always 15 minutes later.

本文是对https://github.com/arieb/python-testdata的粗略翻译

python中强大的testdata库自动生成测试所需要的数据

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