How to Validate Data with a Simple Dict-Based Rules Helper in Python

Validates a dictionary against a set of callable rules, printing pass/fail per field and returning an overall boolean.

Easy Python 3.10+ Aug 9, 2026 Modern tooling 15 views 0 copies

validation dictionary helper data-quality

Python code

34 lines

Python 3.10+

import json
from pathlib import Path
from typing import Any, Callable


def validate_data(
    data: dict[str, Any],
    rules: dict[str, Callable[[Any], bool]],
    path: Path | None = None,
) -> bool:
    """Validate a dict against a set of simple rules."""
    all_valid = True
    for field, validator in rules.items():
        value = data.get(field)
        is_valid = validator(value)
        all_valid = all_valid and is_valid
        print(f"{field}: {value!r} -> {'PASS' if is_valid else 'FAIL'}")
    return all_valid


if __name__ == "__main__":
    sample = {"name": "Alice", "age": 30, "email": "alice@example.com"}
    rules = {
        "name": lambda v: isinstance(v, str) and len(v) > 0,
        "age": lambda v: isinstance(v, int) and v >= 18,
        "email": lambda v: "@" in v if v else False,
    }
    result = validate_data(sample, rules)
    print(f"Overall: {'VALID' if result else 'INVALID'}")

    # Try a failing case
    bad = {"name": "", "age": 12, "email": None}
    print("\nSecond check:")
    validate_data(bad, rules)

Output

stdout

name: 'Alice' -> PASS
age: 30 -> PASS
email: 'alice@example.com' -> PASS
Overall: VALID

Second check:
name: '' -> FAIL
age: 12 -> FAIL
email: None -> FAIL

How it works

The validate_data function takes a dictionary and a rules dictionary where each key maps to a callable that returns a boolean. It uses the .get() method to safely access fields, tolerating missing keys (which become None). The all_valid flag accumulates the AND of each check, and the function prints a clear PASS/FAIL line for each field. This pattern is simple, readable, and easily extendable—you can replace the lambda validators with named functions or type-checked callables without changing the core design.

Common mistakes

Using `data[field]` instead of `.get()`, which raises KeyError on missing keys
Forgetting that `all_valid = all_valid and is_valid` is needed to accumulate (not just per-field)
Writing validators that assume the value exists; handle `None` explicitly in lambdas
Not using `if __name__ == "__main__"` guard, so the demo runs on import

Variations

Use a list of (field, validator) tuples instead of a dict to preserve order and allow duplicate fields
Raise a custom exception listing failed fields instead of printing to stdout

Real-world use cases

Validating user input from a web form before saving to a database.
Checking configuration dictionaries loaded from environment variables or YAML files.
Sanity-checking API response payloads before processing them in a pipeline.

How to Validate Data with a Simple Dict-Based Rules Helper in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Common mistakes

Variations

Real-world use cases

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