【Python + yaml】之yaml文件数据驱动（包括DDT驱动）

写自动化测试代码中，数据驱动传递参数比较方便一些，也便于后期维护，下面介绍两种数据驱动：

下面是test.yaml文件：

start_HRApp:
  ip: 127.0.0.1
  port: 4723
  implicitly_wait: 10
  caps:
    android:
      platformName: Android
      #模拟器
      platformVersion: 6.0
      deviceName: OPPO
      appPackage: com.csksc2b.invertory
      appActivity: com.csks.login.SplashAty
#      noReset: True
#      unicodeKeyboard: True
#      resetKeyboard: True
#      autoGrantPermissions: True
      automationName: uiautomator2
    ios:

①用于一般文件的yaml数据驱动：【个人推荐这个】，它不仅可以用在测试用例，也可以用在其他py文件中

from appium import webdriver
import yaml
import os

def des_caps():

    # 基础路径
    base_dir = os.path.dirname(os.path.dirname(__file__))
    # yaml路径
    yaml_path = base_dir + "/data/ddt_data_file.yaml"
    # 获取yaml的数据
    with open(yaml_path,'r',encoding='utf-8') as file:
        data = yaml.load(file)
    start = data['start_HRApp']
    Cap = start['caps']['android']

    driver = webdriver.Remote("http://"+ str(start['ip']) +":"+ str(start['port']) +"/wd/hub",Cap)
    driver.implicitly_wait(10)

    return driver

========================================================= 

yaml文件

case01:
  url: https://ascendas.17mine.cn/basic/pick/selectPage
  headers:
    Authorization: eyJhbGciOiJIUzI1NiJ9.eyJuZWVkRWRpdCI6LTEsImxvZ2luVGltZSI6MTU5MDYyOTYwMTU0NSwibG9naW5XYXkiOjEsInVzZXJOYW1lIjoi5byg55WFIiwidXNlcklkIjoiMTI1NDI5NDE4NzAzODM0NzI2NCIsImxvZ2luU291cmNlIjotMSwiYWNjb3VudCI6IjEzNjQyMDQwNjMxIiwiZXhwIjoxNTkwNjcyODAxfQ.qydhemA3sGfrBuHFWcTi8OdaOcm7hvIpgErtkQ2OVBo
  payload:
    pageNum: 1
    pageSize: 1
    user_id: 1254294187038347264
    userId: 1254294187038347264
    infos_id: 1207504682260500480
    infoId: 1207504682260500480

或者自定义一个方法：

    def yamlData(self):
        '''获取yaml数据'''
        self.path = os.path.dirname(os.path.abspath(__file__))
        # yaml路径
        self.yamlPath = self.path + "/data/case_data.yaml"
        # 获取yaml数据
        with open(self.yamlPath, 'r', encoding='utf-8') as file:
            data = yaml.load(file)
        return data

然后再引用：

    def test_request01(self):
        case01 = self.yamlData()['case01']
        url = case01['url']
        payload = case01['payload']
        headers = case01['headers']

        r = requests.post(url,params=payload,headers=headers).json()
        self.assertEqual(r['data']['records'][0]['stockOutName'],"0506测试仓库")

②用于测试用例中的yaml数据驱动（DDT）：

yaml文件：

case02:
  url: https://www.v2ex.com/api/nodes/show.json
  payload:
    name: python

import unittest
import requests
import os
import yaml
from ddt import ddt, data, file_data, unpack


@ddt
class TestResquest(unittest.TestCase):
    @file_data('./data/case_data.yaml')
    @unpack
    def test_request02(self,**kwargs):
        url = kwargs['url']
        payload = kwargs['payload']
        r = requests.get(url,params=payload).json()
        self.assertEqual(r['id'],901)

但是DDT有一点不好的是不灵活，如果有两个case的yaml，想获取url，他会把两个URL一块执行再一个用例中

case01:
  url: https://ascendas.17mine.cn/basic/assemble/selectPage
  headers:
    Authorization: eyJhbGciOiJIUzI1NiJ9.eyJuZWVkRWRpdCI6LTEsImxvZ2luVGltZSI6MTU5MTE0NDk2NTExOSwibG9naW5XYXkiOjEsInVzZXJOYW1lIjoi5byg55WFIiwidXNlcklkIjoiMTI1NDI5NDE4NzAzODM0NzI2NCIsImxvZ2luU291cmNlIjotMSwiYWNjb3VudCI6IjEzNjQyMDQwNjMxIiwiZXhwIjoxNTkxMTg4MTY1fQ.OiSBpkRJMZsABAlKhfo4P2cmZuqk6V63vDACZBY5Xs8
  payload:
    pageNum: 1
    pageSize: 1
#    user_id: 1254294187038347264
#    userId: 1254294187038347264
#    infos_id: 1207504682260500480
    infoId: 1207504682260500480


case02:
  url: https://www.v2ex.com/api/nodes/show.json
  payload:
    name: python

 如果想测试重复的用例可以适用这个。但是变化较多的字段的用例不适用。

如下测试重复的用例：

用例1:
  data1:
    - keys: "yaml01"
    - keys: "yaml02"
  data2:
    - keys: "yaml03"
    - keys: "yaml04"
用例2:
  data1:
    - keys: "yaml05"
    - keys: "yaml06"
  data2:
    - keys: "yaml07"
    - keys: "yaml08"
用例3:
  data1:
    - keys: "yaml09"
    - keys: "yaml10"
  data2:
    - keys: "yaml11"
    - keys: "yaml12"

代码：

import unittest
import requests
import os
import yaml
from ddt import ddt, data, file_data, unpack


@ddt
class TestResquest(unittest.TestCase):
    @file_data("../data/ddt_data_file.yaml")
    @unpack
    def test_baiduSearch03(self,**kwargs):
        keys = kwargs['data1'][1]['keys']
        print("第三组测试用例：",keys)
        self.baidu_search(keys)
        self.assertEqual(self.driver.title, keys + "_百度搜索", msg="标题不正确！")

一个用例可以执行三遍

优缺点：

①一般的yaml方法，可以适用于任何文件，只是写法有点繁琐，适用于多种用例，较灵活。

②DDT的yaml方法，只能用于测试用例文件中，写法简单，适用于一种重复性用例，不灵活。

拓展：

YAML、YML在线编辑(校验)器

把yaml文件转换成json