序列化和反序列化、pytest-DDT数据驱动

序列化

序列化就是将对象转化成文件

python转成json

import jsondata = {"数字": [1, 1.1, -1],"字符串": ["aaaa", 'bbbb'],"布尔值": [True, False],"空值": None,"列表": [[1, 2, 3], [4, 5, 6], [7, 8, 9]],"字典": [{"a": 1, "b": 2}, {"c": 3}]
}with open("data.json", "w", encoding="utf-8") as f:s = json.dumps(data, ensure_ascii=False)f.write(s)

python转成yaml

import yamldata = {"数字": [1, 1.1, -1],"字符串": ["aaaa", 'bbbb'],"布尔值": [True, False],"空值": None,"列表": [[1, 2, 3], [4, 5, 6], [7, 8, 9]],"字典": [{"a": 1, "b": 2}, {"c": 3}]
}with open("data.yaml", "w", encoding="utf-8") as f:s = yaml.safe_dump(data, allow_unicode=True)f.write(s)

反序列化

反序列化就是将文件转化成对象

json转成python

import jsonwith open("data.json", "r", encoding="utf-8") as f:s = json.loads(f.read())print(s)

yaml转成python

import yamlwith open("data.yaml", "r", encoding="utf-8") as f:s = yaml.safe_load(f.read())print(s)# yaml可以完全兼容json
with open("data.json", "r", encoding="utf-8") as f:s = yaml.safe_load(f.read())print(s)

pytest的DDT数据驱动

DDT：数据驱动测试

DDT数据驱动的本质：测试框架根据数据文件的内容，来决定如何执行测试用例

实现思路：

（1）将用例参数的数据存放在（.json、.yaml等）文件中

（2）读取文件中的数据

（3）将数据传递给pytest（根据数据创建用例）

（4）在用例中使用数据

举例：

将从ddt_yaml.yaml文件中读取的数据内容打包成列表传递给@pytest.mark.parametrize()

yaml里面的内容（这里给出的数据不是纯yaml语法的格式）：

- [1, 2]
- [2, 3]
- [3, 4]
- [0, -1]

用例实现： 

mport pytest
import yamldef add(a, b):return a + bwith open("ddt_yaml.yaml", "r", encoding="utf-8") as f:l = yaml.safe_load(f.read())print(l)@pytest.mark.parametrize("a, b",l,
)
def test_abc(a, b):c = add(a, b)assert c == a + b

优点

1. 方便用例的增加和减少（只需要改变文件里面的数据组数就可以控制用例数量）

2. 方便对用例内容的修改