517 lines
12 KiB
Plaintext
517 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Item(name='iPhone', price=0, number=0, categories=[Category(name='', level=1)])"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import typing as t\n",
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"\n",
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"from pydantic import BaseModel\n",
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"\n",
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"\n",
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"class Category(BaseModel):\n",
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" name: str = \"\"\n",
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" level: int = 1\n",
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"\n",
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"\n",
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"class Item(BaseModel):\n",
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" name: str\n",
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" price: t.Union[int, float] = 0\n",
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" number: int = 0\n",
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" categories: t.List[Category] = [Category()]\n",
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"\n",
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"\n",
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"Item(name=\"iPhone\")\n",
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"Item(name='iPhone', price=0, number=0, categories=[Category(name='', level=1)])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 额外的类型注解"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pydantic import BaseModel, FilePath, HttpUrl, IPvAnyAddress, PostgresDsn\n",
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"from pydantic.color import Color\n",
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"\n",
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"class FileSystem(BaseModel):\n",
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" path: FilePath\n",
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"\n",
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"\n",
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"class Link(BaseModel):\n",
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" url: HttpUrl\n",
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"\n",
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"\n",
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"class Network(BaseModel):\n",
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" ip: IPvAnyAddress\n",
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"\n",
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"\n",
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"class PostgreDatabase(BaseModel):\n",
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" dsn: PostgresDsn\n",
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"\n",
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"\n",
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"class WebColor(BaseModel):\n",
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" value: Color"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Item(name='iphone', number=0)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from pydantic import constr, conint\n",
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"\n",
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"class Item(BaseModel):\n",
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" name: constr(strip_whitespace=True, min_length=2, max_length=10)\n",
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" number: conint(ge=0) = 0\n",
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"\n",
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"\n",
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"Item(name=\" iphone \")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Item(name=\"iPhone\", number=-1) # raise ValidationError here!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 数据校验"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import typing as t\n",
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"from dataclasses import dataclass\n",
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"from pydantic import BaseModel, validator\n",
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"\n",
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"@dataclass\n",
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"class Category:\n",
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" level: int = 1\n",
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" name: str = \"\"\n",
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"\n",
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"class Item(BaseModel):\n",
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" name: str\n",
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" price: t.Union[int, float] = 0\n",
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" number: int = 0\n",
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" categories: t.List[Category] = [Category()]\n",
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"\n",
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"\n",
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" @validator(\"price\", \"number\")\n",
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" def prevent_negative_number(cls, v):\n",
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" if v < 0:\n",
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" raise ValueError(\"can't set a negative number\")\n",
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" return v"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Item(name=\"iPhone\", price=-1) # raise ValidationError here!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import typing as t\n",
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"from dataclasses import field\n",
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"\n",
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"from pydantic import validator\n",
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"from pydantic.dataclasses import dataclass # <-- use pydantic.dataclasses.dataclass\n",
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"\n",
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"\n",
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"@dataclass\n",
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"class Category:\n",
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" name: str = \"\"\n",
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" level: int = 1\n",
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"\n",
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"\n",
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"def default_categories():\n",
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" return [Category()]\n",
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"\n",
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"\n",
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"@dataclass\n",
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"class Item:\n",
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" name: str\n",
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" price: t.Union[int, float] = field(default=0)\n",
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" number: int = field(default=0)\n",
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" categories: t.List[Category] = field(default_factory=default_categories)\n",
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"\n",
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" @validator(\"price\", \"number\")\n",
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" def prevent_negative_number(cls, v):\n",
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" if v < 0:\n",
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" raise ValueError(\"can't set a negative number\")\n",
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" return v"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"import typing as t\n",
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"\n",
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"from pydantic import BaseModel, validator\n",
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"from pydantic.dataclasses import dataclass\n",
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"\n",
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"\n",
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"@dataclass\n",
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"class Category:\n",
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" name: str = \"\"\n",
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" level: int = 1\n",
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"\n",
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"\n",
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"\n",
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"class ItemCode(str):\n",
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" @classmethod\n",
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" def __get_validators__(cls):\n",
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" yield cls.validate\n",
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"\n",
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" @classmethod\n",
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" def validate(cls, v):\n",
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" if not v.startswith(\"ITEM\"):\n",
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" raise ValueError(\"invalid item code format\")\n",
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" return v\n",
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"\n",
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"\n",
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"class Item(BaseModel):\n",
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" name: str\n",
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" itemcode: ItemCode\n",
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" price: t.Union[int, float] = 0\n",
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" number: int = 0\n",
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" categories: t.List[Category] = [Category()]\n",
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"\n",
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" @validator(\"price\", \"number\")\n",
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" def prevent_negative_number(cls, v):\n",
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" if v < 0:\n",
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" raise ValueError(\"can't set a negative number\")\n",
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" return v"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Item(name='iPhone', itemcode='ITEM31415926', price=0, number=0, categories=[Category(name='', level=1)])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Item(name=\"iPhone\", itemcode=\"ITEM31415926\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Item(name=\"iPhone\", itemcode=\"aabbcc\") # raise ValidationError here!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"import typing as t\n",
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"\n",
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"from pydantic import BaseModel, validator\n",
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"from pydantic.dataclasses import dataclass\n",
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"\n",
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"\n",
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"class ItemValidator:\n",
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" @classmethod\n",
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" def prevent_negative_number(cls, v):\n",
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" if v < 0:\n",
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" raise ValueError(\"can't set a negative number\")\n",
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" return v\n",
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"\n",
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" @classmethod\n",
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" def has_itemcode_preffix(cls, v: str):\n",
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" if not v.startswith(\"ITEM\"):\n",
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" raise ValueError(\"invalid item code format\")\n",
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" return v\n",
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"\n",
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"\n",
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"@dataclass\n",
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"class Category:\n",
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" name: str = \"\"\n",
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" level: int = 1\n",
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"\n",
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"\n",
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"\n",
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"class Item(BaseModel):\n",
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" name: str\n",
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" itemcode: str\n",
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" price: t.Union[int, float] = 0\n",
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" number: int = 0\n",
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" categories: t.List[Category] = [Category()]\n",
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"\n",
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" check_number = validator(\"price\", \"number\")(ItemValidator.prevent_negative_number)\n",
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" check_itemcode = validator(\"itemcode\")(ItemValidator.has_itemcode_preffix)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 导出数据类"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"import typing as t\n",
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"\n",
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"from pydantic import BaseModel\n",
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"\n",
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"\n",
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"class Category(BaseModel):\n",
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" name: str = \"\"\n",
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" level: int = 1\n",
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"\n",
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"\n",
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"class Item(BaseModel):\n",
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" name: str\n",
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" price: t.Union[int, float] = 0\n",
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" number: int = 0\n",
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" categories: t.List[Category] = [Category()]\n",
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"\n",
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"\n",
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"item = Item(\n",
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" name=\"iPhone\",\n",
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" price=6999,\n",
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" number=1000,\n",
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" categories=[Category(name=\"Phone\")],\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'name': 'iPhone',\n",
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" 'price': 6999,\n",
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" 'number': 1000,\n",
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" 'categories': [{'name': 'Phone', 'level': 1}]}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"item.dict()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'{\"name\": \"iPhone\", \"price\": 6999, \"number\": 1000, \"categories\": [{\"name\": \"Phone\", \"level\": 1}]}'"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"item.json()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'name': 'iPhone',\n",
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" 'price': 6999,\n",
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" 'categories': [{'name': 'Phone', 'level': 1}]}"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"item.dict(exclude={\"number\": True})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 从 ORM 中进行转换"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"import typing as t\n",
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"from datetime import datetime\n",
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"\n",
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"import peewee as pw\n",
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"from pydantic import BaseModel, Field\n",
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"\n",
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"db = pw.SqliteDatabase(\":memory:\")\n",
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"\n",
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"\n",
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"class NoteORM(pw.Model):\n",
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" id = pw.AutoField()\n",
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" title = pw.CharField()\n",
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" content = pw.TextField()\n",
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" tags = pw.CharField()\n",
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" create_at = pw.DateTimeField(default=datetime.now())\n",
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"\n",
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" class Meta:\n",
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" database = db\n",
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"\n",
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"\n",
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"class NoteModel(BaseModel):\n",
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" id: t.Optional[int] = Field(example=\"null\")\n",
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" title: str\n",
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" content: str\n",
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" tags: t.Optional[t.List[str]] = Field(example=\"null\")\n",
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"\n",
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" class Config:\n",
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" orm_mode = True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'id': None, 'title': '测试', 'content': '# Hello, world\\n', 'tags': ['技术分享']}"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"note_db = NoteORM(title=\"测试\", content=\"# Hello, world\\n\", tags=[\"技术分享\"])\n",
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"note = NoteModel.from_orm(note_db)\n",
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"note.dict()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.0 ('pandas-startup')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "13977d4cc82dee5f9d9535ceb495bd0ab12a43c33c664e5f0d53c24cf634b67f"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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