import pandas as pd


df = pd.DataFrame(
    {
        "order_id": ["1001", "1002", "BAD", "1004"],
        "ordered_at": ["2026-06-01", "2026-06-02", "not a date", "2026-06-04"],
        "quantity": ["2", "5", "", "3"],
        "paid": ["true", "false", "true", "false"],
        "region": ["EMEA", "APAC", "EMEA", "AMER"],
    }
)

print(f"pandas {pd.__version__}")
print()

print("source dtypes")
print(df.dtypes)
print()

converted = df.copy()
converted["order_id"] = pd.to_numeric(converted["order_id"], errors="coerce").astype("Int64")
converted["quantity"] = pd.to_numeric(converted["quantity"], errors="coerce").astype("Int64")
converted["ordered_at"] = pd.to_datetime(converted["ordered_at"], errors="coerce")
converted["paid"] = converted["paid"].map({"true": True, "false": False}).astype("boolean")
converted["region"] = converted["region"].astype("category")

converted = converted.convert_dtypes()

print("converted dtypes")
print(converted.dtypes)
print()

print("converted values")
print(converted.to_string(index=False))
print()

failed = converted[
    converted.filter(["order_id", "quantity", "ordered_at", "paid"]).isna().any(axis=1)
]

print("rows with failed conversions")
print(failed.to_string(index=False))