Python学习笔记-Schema数据结构及类型校验
Python学习笔记-Schema数据结构及类型校验
使用
schema库来执行数据结构的校验。schema是一个简单而强大的库，用于定义和验证 Python 数据结构的约束
And
And代表必选，数据结构里必须包含这个 schema，如下方声明了name，则代表这个name必须存在与字典中
from schema import Schema, And, SchemaError
user_schema = Schema([
{
"name": And(str)
}
])
user_data_1 = [{
"name": "jruing",
}]
user_data_2 = [{
"name": 666,
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing'}]
数据校验异常user_result_2:Or({'name': And(<class 'str'>)}) did not validate {'name': 666}
Key 'name' error:
666 should be instance of 'str'
Or
Or代表值的类型必须为某两个类型，比如int或float，tuple或list
from schema import Schema, And, SchemaError, Or
user_schema = Schema([
{
"name": And(str),
"money": Or(int,float)
}
])
user_data_1 = [{
"name": "jruing",
"money": 1000,
}]
user_data_2 = [{
"name": "jruing",
"money": 1000.1,
}]
user_data_3 = [{
"name": "jruing",
"money": "1000.1",
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
try:
user_result_3 = user_schema.validate(user_data_3)
print(f"数据校验user_result_3：{user_result_3}")
except SchemaError as e:
print(f"数据校验异常user_result_3:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing', 'money': 1000, 'addr': '中国', 'country': '中国', 'email': '123456@qq.com'}]
数据校验异常user_result_2:Or({'name': And(<class 'str'>), 'money': Or(<class 'int'>, <class 'float'>), Optional('addr'): And(<class 'str'>), Optional('email'): And(<class 'str'>, Regex('^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$', flags=re.IGNORECASE)), 'country': Const('中国')}) did not validate {'name': 'jruing', 'money': 1000.1, 'addr': '1111', 'country': '山西', 'email': '123456'}
Key 'country' error:
'中国' does not match '山西'
Const
Const代表值必须为指定的某个常量，比如下面的country必须为中国
import re
from schema import Schema, And, SchemaError, Or, Optional, Regex, Const
user_schema = Schema([
{
"name": And(str),
"money": Or(int, float),
Optional("addr"): And(str),
Optional("email"): And(str, Regex(r'^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$', flags=re.I)),
"country": Const("中国")
}
])
user_data_1 = [{
"name": "jruing",
"money": 1000,
"addr": "中国",
"country": "中国",
"email": "123456@qq.com"
}]
user_data_2 = [{
"name": "jruing",
"money": 1000.1,
"addr": "1111",
"country": "山西",
"email": "123456"
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing', 'money': 1000, 'addr': '中国', 'country': '中国', 'email': '123456@qq.com'}]
数据校验异常user_result_2:Or({'name': And(<class 'str'>), 'money': Or(<class 'int'>, <class 'float'>), Optional('addr'): And(<class 'str'>), Optional('email'): And(<class 'str'>, Regex('^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$', flags=re.IGNORECASE)), 'country': Const('中国')}) did not validate {'name': 'jruing', 'money': 1000.1, 'addr': '1111', 'country': '山西', 'email': '123456'}
Key 'country' error:
'中国' does not match '山西'
Optional
Optional代表这个key或者元素为非必选，可有可无
from schema import Schema, And, SchemaError, Or, Optional
user_schema = Schema([
{
"name": And(str),
"money": Or(int, float),
Optional("addr"): And(str)
}
])
user_data_1 = [{
"name": "jruing",
"money": 1000,
"addr": "中国"
}]
user_data_2 = [{
"name": "jruing",
"money": 1000.1,
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing', 'money': 1000, 'addr': '中国'}]
数据校验user_result_2：[{'name': 'jruing', 'money': 1000.1}]
Use
Use函数允许你在验证前对数据进行转换。这对于在验证之前对数据进行清理、格式化或其他操作非常有用。
import re
from schema import Schema, And, SchemaError, Or, Optional, Regex, Const, Use
user_schema = Schema([
{
"name": And(str),
"money": Or(int, float),
"age": Use(int),
Optional("addr"): And(str),
Optional("email"): And(str, Regex(r'^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$', flags=re.I)),
Optional("country"): Const("中国")
}
])
user_data_1 = [{
"name": "jruing",
"money": 1000,
"age": 11,
"addr": "中国",
"country": "中国",
"email": "123456@qq.com"
}]
user_data_2 = [{
"name": "jruing",
"money": 1000.1,
"age": "18",
"addr": "1111",
"email": "123456@qq.com"
}]
user_data_3 = [{
"name": "jruing",
"money": 1000.1,
"age": "fff",
"addr": "1111",
"email": "123456@qq.com"
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
try:
user_result_3 = user_schema.validate(user_data_3)
print(f"数据校验user_result_3：{user_result_3}")
except SchemaError as e:
print(f"数据校验异常user_result_3:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing', 'money': 1000, 'age': 11, 'addr': '中国', 'country': '中国', 'email': '123456@qq.com'}]
数据校验user_result_2：[{'name': 'jruing', 'money': 1000.1, 'age': 18, 'addr': '1111', 'email': '123456@qq.com'}]
数据校验异常user_result_3:Or({'name': And(<class 'str'>), 'money': Or(<class 'int'>, <class 'float'>), 'age': Use(<class 'int'>), Optional('addr'): And(<class 'str'>), Optional('email'): And(<class 'str'>, Regex('^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$', flags=re.IGNORECASE)), Optional('country'): Const('中国')}) did not validate {'name': 'jruing', 'money': 1000.1, 'age': 'fff', 'addr': '1111', 'email': '123456@qq.com'}
Key 'age' error:
int('fff') raised ValueError("invalid literal for int() with base 10: 'fff'")
Regex
通过正则表达式，对值进行匹配校验，常用的就是邮箱，手机号等场景
import re
from schema import Schema, And, SchemaError, Or, Optional, Regex
user_schema = Schema([
{
"name": And(str),
"money": Or(int, float),
Optional("addr"): And(str),
Optional("email"): And(str, Regex(r'^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$', flags=re.I))
}
])
user_data_1 = [{
"name": "jruing",
"money": 1000,
"addr": "中国",
"email": "123456@qq.com"
}]
user_data_2 = [{
"name": "jruing",
"money": 1000.1,
"addr": "1111",
"email": "123456"
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing', 'money': 1000, 'addr': '中国', 'email': '123456@qq.com'}]
数据校验异常user_result_2:Or({'name': And(<class 'str'>), 'money': Or(<class 'int'>, <class 'float'>), Optional('addr'): And(<class 'str'>), Optional('email'): And(<class 'str'>, Regex('^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$', flags=re.IGNORECASE))}) did not validate {'name': 'jruing', 'money': 1000.1, 'addr': '1111', 'email': '123456'}
Key 'email' error:
Regex('^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$', flags=re.IGNORECASE) does not match '123456'
Forbidden
Forbidder允许你定义一些不被允许的值，如果数据中包含这些值，验证将失败，下面的例子表示密码password字段的值不允许设置为123456
import re
from schema import Schema, And, SchemaError, Or, Optional, Regex, Const, Use, Forbidden
user_schema = Schema([
{
"name": And(str),
"money": Or(int, float),
"age": Use(int),
Optional("addr"): And(str),
Optional("email"): And(str, Regex(r'^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$', flags=re.I)),
Optional("country"): Const("中国"),
"password": And(str, Forbidden("123456"))
}
])
user_data_1 = [{
"name": "jruing",
"money": 1000,
"age": 11,
"addr": "中国",
"country": "中国",
"email": "123456@qq.com",
"password": "123456"
}]
user_data_2 = [{
"name": "jruing",
"money": 1000.1,
"age": "18",
"addr": "1111",
"email": "123456@qq.com",
"password": "1234561"
}]
try:
user_result_1 = user_schema.validate(user_data_1)
print(f"数据校验user_result_1：{user_result_1}")
except SchemaError as e:
print(f"数据校验异常user_result_1:{e}")
try:
user_result_2 = user_schema.validate(user_data_2)
print(f"数据校验user_result_2：{user_result_2}")
except SchemaError as e:
print(f"数据校验异常user_result_2:{e}")
==========调用结果==========
数据校验user_result_1：[{'name': 'jruing', 'money': 1000, 'age': 11, 'addr': '中国', 'country': '中国', 'email': '123456@qq.com', 'password': '123456'}]
数据校验异常user_result_2:Or({'name': And(<class 'str'>), 'money': Or(<class 'int'>, <class 'float'>), 'age': Use(<class 'int'>), Optional('addr'): And(<class 'str'>), Optional('email'): And(<class 'str'>, Regex('^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$', flags=re.IGNORECASE)), Optional('country'): Const('中国'), 'password': And(<class 'str'>, Forbidden('123456'))}) did not validate {'name': 'jruing', 'money': 1000.1, 'age': '18', 'addr': '1111', 'email': '123456@qq.com', 'password': '1234561'}
Key 'password' error:
'123456' does not match '1234561'
本文来自博客园，作者：Jruing，转载请注明原文链接：https://www.cnblogs.com/jruing/p/17845434.html
