generate_from_schema Examples

generate_from_schema Examples

Plain mapping

df = generate_from_schema(
    {"customer_id": "string", "customer_name": "string", "age": "int"},
    rows=1000,
)

Compact DDL

df = generate_from_schema(
    "customer_id string, customer_name string, age int, created_at timestamp",
    rows=1000,
)

Pandas DataFrame

import pandas as pd

empty = pd.DataFrame(
    {
        "customer_id": pd.Series(dtype="string"),
        "age": pd.Series(dtype="int64"),
    }
)
df = generate_from_schema(empty, rows=1000)

Rules and validation

df, report = generate_from_schema(
    schema,
    rows=1000,
    custom_rules={
        "customer_id": {"prefix": "CUST"},
        "age": {"min": 18, "max": 85},
    },
    validate=True,
    return_report=True,
)

Spark

spark_df = generate_from_schema(spark_struct_type, rows=1000, engine="spark")
spark_df.write.mode("overwrite").parquet("synthetic/customers")

See Supported Schema Input Types before adapting an example to JSON Schema, YAML, full SQL DDL, ORM, Pydantic, or dataclass inputs.