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docs/en/docs/tutorial/sql-databases.md
The `__tablename__` attribute tells SQLAlchemy the name of the table to use in the database for each of these models. ### Create model attributes/columns Now create all the model (class) attributes. Each of these attributes represents a column in its corresponding database table. We use `Column` from SQLAlchemy as the default value.
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docs_src/sql_databases/sql_app_py39/models.py
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String from sqlalchemy.orm import relationship from .database import Base class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) email = Column(String, unique=True, index=True) hashed_password = Column(String) is_active = Column(Boolean, default=True) items = relationship("Item", back_populates="owner")
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docs/en/docs/tutorial/dependencies/dependencies-with-yield.md
## Execution of dependencies with `yield` The sequence of execution is more or less like this diagram. Time flows from top to bottom. And each column is one of the parts interacting or executing code. ```mermaid sequenceDiagram participant client as Client participant handler as Exception handler participant dep as Dep with yield
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docs_src/sql_databases/sql_app/models.py
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String from sqlalchemy.orm import relationship from .database import Base class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) email = Column(String, unique=True, index=True) hashed_password = Column(String) is_active = Column(Boolean, default=True) items = relationship("Item", back_populates="owner")
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docs_src/sql_databases/sql_app_py310/models.py
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String from sqlalchemy.orm import relationship from .database import Base class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) email = Column(String, unique=True, index=True) hashed_password = Column(String) is_active = Column(Boolean, default=True) items = relationship("Item", back_populates="owner")
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docs_src/async_sql_databases/tutorial001.py
database = databases.Database(DATABASE_URL) metadata = sqlalchemy.MetaData() notes = sqlalchemy.Table( "notes", metadata, sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), sqlalchemy.Column("text", sqlalchemy.String), sqlalchemy.Column("completed", sqlalchemy.Boolean), ) engine = sqlalchemy.create_engine( DATABASE_URL, connect_args={"check_same_thread": False} )
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docs/em/docs/tutorial/sql-databases.md
{!> ../../../docs_src/sql_databases/sql_app_py310/schemas.py!} ``` #### 🇸🇲 👗 & Pydantic 👗 👀 👈 🇸🇲 *🏷* 🔬 🔢 ⚙️ `=`, & 🚶♀️ 🆎 🔢 `Column`, 💖: ```Python name = Column(String) ``` ⏪ Pydantic *🏷* 📣 🆎 ⚙️ `:`, 🆕 🆎 ✍ ❕/🆎 🔑: ```Python name: str ``` ✔️ ⚫️ 🤯, 👆 🚫 🤚 😕 🕐❔ ⚙️ `=` & `:` ⏮️ 👫.
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tests/test_tutorial/test_body/test_tutorial001_py310.py
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tests/test_tutorial/test_body/test_tutorial001.py
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docs/zh/docs/tutorial/sql-databases.md
{!> ../../../docs_src/sql_databases/sql_app/schemas.py!} ``` #### SQLAlchemy 风格和 Pydantic 风格 请注意,SQLAlchemy*模型*使用 `=`来定义属性,并将类型作为参数传递给`Column`,例如: ```Python name = Column(String) ``` 虽然 Pydantic*模型*使用`:` 声明类型,但新的类型注释语法/类型提示是: ```Python name: str ``` 请牢记这一点,这样您在使用`:`还是`=`时就不会感到困惑。 ### 创建用于读取/返回的Pydantic*模型/模式*
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