## schema_from_tbl() Create a Schema from an existing table with inferred Field constraints. Usage ``` python schema_from_tbl( tbl, *, infer_constraints=True, categorical_threshold=20, detect_presets=True, sample_size=None ) ``` This is the functional form of `Schema.from_table()`. It inspects the actual values in the table to infer rich constraints (min/max, uniqueness, null rates, allowed values, presets) suitable for synthetic data generation via `schema.generate()` or [generate_dataset()](generate_dataset.md#pointblank.generate_dataset). ## Parameters `tbl: Any` A Polars DataFrame, Pandas DataFrame, or Ibis table (DuckDB, SQLite, etc.). `infer_constraints: bool = ``True` When `True` (default), inspect values to infer min/max, uniqueness, null rates, etc. When `False`, behave like `Schema(tbl=df)` (dtype only). `categorical_threshold: int | float = ``20` If a column has \<= this many unique values (int) or this fraction of total rows (float between `0` and `1`), treat it as categorical and populate `allowed=`. Default is `20`. `detect_presets: bool = ``True` Attempt to match string columns to known generation presets (e.g., email, url, phone_number) based on column name heuristics and value validation. Default is `True`. `sample_size: int | None = None` If set, sample this many rows before analysis (useful for very large tables). `None` means use all rows. ## Returns `Schema` A Schema populated with Field objects containing inferred constraints, ready for use with `schema.generate()` or [generate_dataset()](generate_dataset.md#pointblank.generate_dataset). ## Examples ``` python import pointblank as pb import polars as pl df = pl.DataFrame({ "user_id": list(range(1, 51)), "email": [f"user{i}@example.com" for i in range(50)], "age": [20 + i % 50 for i in range(50)], "status": ["active", "pending", "inactive"] * 16 + ["active", "pending"], }) schema = pb.schema_from_tbl(df) print(schema) ``` Pointblank Schema user_id: IntField(dtype='Int64', nullable=False, null_probability=0.0, unique=True, min_val=1, max_val=50, allowed=None) email: StringField(dtype='String', nullable=False, null_probability=0.0, unique=True, min_length=None, max_length=None, pattern=None, preset='email', allowed=None) age: IntField(dtype='Int64', nullable=False, null_probability=0.0, unique=True, min_val=20, max_val=69, allowed=None) status: StringField(dtype='String', nullable=False, null_probability=0.0, unique=False, min_length=None, max_length=None, pattern=None, preset=None, allowed=['active', 'inactive', 'pending']) Generate synthetic data matching the original's characteristics: ``` python pb.preview(schema.generate(n=10, seed=23)) ```

PolarsRows10Columns4
	user_id Int64	email String	age Int64	status String
1	50	doris.martin@yandex.com	69	inactive
2	19	ngonzalez74@yahoo.com	38	active
3	6	jessica379@protonmail.com	25	active
4	2	george_evans@yahoo.com	21	pending
5	38	p_williams@outlook.com	57	inactive
6	20	andreamitchell@mail.com	39	inactive
7	28	maria.valentine@mail.com	47	inactive
8	25	vwalker@gmail.com	44	pending
9	34	brenda.lopez@zoho.com	53	inactive
10	23	laurendavis@aol.com	42	active