無論是效能測試, 還是大資料的測試, 甚至基本的功能測試, 造資料都是一件棘手的事
拋開資料間的關聯關係不談, 各種型別的資料長短不一, 格式不一, 單純的複製貼上不利於識別, 也不符合一般的資料場景
有幸得知faker模組, 解決了大部分的資料構造問題
語法特別簡單, 學習參考:
蔣建山西省
湖南省建市瀋北新深圳街a座 238009
18770641692
硬體工程師構造資料的思路大體相似, 採用隨機方式
各類資料通過不同的provider實現,provider定義資料模板和具體實現
內建provider有address, color, compnay, internet, person, geo, user_agent等, 不過並不是所有型別都適用於zh_cn
我們看一下如何生成姓名
class
provider
(baseprovider)
: formats =
['} }'
] first_names =
['john'
,'jane'
] last_names =
['doe'
]def
name
(self)
:"""
:example 'john doe'
"""pattern = self.random_element(self.formats)
return self.generator.parse(pattern)..
..
class
provider
(personprovider)
: formats =
["}}"
] first_names_male =
["偉"
,"強"
,"磊"
,"洋"
,"勇"
,"軍"
,"傑"
,"濤"
,"超"
,"明"
,"剛"
,"平"
,"輝"
,"鵬"
,"華"
,"飛"
,"鑫"
,"波"
,"斌"
,"宇"
,"浩"
,"凱"
,"健"
,"俊"
,"帆"
,"帥"
,"旭"
,"寧"
,"龍"
,"林"
,"歡"
,"佳"
,"陽"
,"建華"
,"亮"
,"成"
,"建"
,"峰"
,"建國"
,"建軍"
,"晨"
,"瑞"
,"志強"
,"兵"
,"雷"
,"東"
,"博"
,"彬"
,"坤"
,"想"
,"巖"
,"楊"
,"文"
,"利"
,"楠"
,"紅霞"
,"建平",]
first_names_female =
["芳"
,"娜"
,"敏"
,"靜"
,"秀英"
,"麗"
,"豔"
,"娟"
,"霞"
,"秀蘭"
,"燕"
,"玲"
,"桂英"
,"丹"
,"萍"
,"紅"
,"玉蘭"
,"桂蘭"
,"英"
,"梅"
,"莉"
,"秀珍"
,"婷"
,"玉梅"
,"玉珍"
,"鳳英"
,"晶"
,"玉英"
,"穎"
,"雪"
,"慧"
,"紅梅"
,"倩"
,"琴"
,"蘭英"
,"暢"
,"雲"
,"潔"
,"柳"
,"淑珍"
,"春梅"
,"海燕"
,"冬梅"
,"秀榮"
,"桂珍"
,"瑩"
,"秀雲"
,"桂榮"
,"秀梅"
,"麗娟"
,"婷婷"
,"玉華"
,"琳"
,"雪梅"
,"淑蘭"
,"麗麗"
,"玉"
,"秀芳"
,"欣"
,"淑英"
,"桂芳"
,"麗華"
,"丹丹"
,"桂香"
,"淑華"
,"榮"
,"秀華"
,"桂芝"
,"小紅"
,"金鳳"
,"瑜"
,"桂花"
,"璐"
,"鳳蘭",]
first_names = first_names_male + first_names_female
...
formats定義了姓名的格式, 屬性由一系列樣本資料構成, 通過隨機方式獲取, 格式中的佔位符(last_name, first_name)則通過正規表示式完成替換 使用faker造假資料
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