SchemaSnap
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CSV to SQLAlchemy Model

A SQLAlchemy declarative model needs a __tablename__, a Column() call per field with the right SQLAlchemy type (Integer, Numeric, DateTime, and so on), and primary_key=True on the key column. Writing that from a raw CSV means re-deriving the same type decisions you would make for SQL, in Python syntax.

SchemaSnap runs the same inference once and emits it as a ready-to-paste class Base subclass, so the Python model and the SQL table it maps to are guaranteed to agree, because they came from the same schema.

Example

Run on a small sample CSV at build time, so this is real output, not a mockup.

Table structure (Postgres baseline)

CREATE TABLE customers (
  id SERIAL PRIMARY KEY,
  name VARCHAR(32) NOT NULL,
  email VARCHAR(32) NOT NULL,
  plan VARCHAR(32) NOT NULL,
  price NUMERIC(10,2) NOT NULL,
  signup_date DATE NOT NULL
);

Generated SQLAlchemy model

class Customers(Base):
    __tablename__ = 'customers'
    id = Column(Integer, primary_key=True)
    name = Column(String(32), nullable=False)
    email = Column(String, nullable=False)
    plan = Column(String(32), nullable=False)
    price = Column(Numeric, nullable=False)
    signup_date = Column(Date, nullable=False)

Try it

Drop a .csv file here, or click to choose one. Or just paste below.

Outputs

Paste or drop a CSV, then hit Convert. Nothing you enter here leaves your browser.

Other conversions