XenValidator 0.2.0

XenValidator is schema validation library for Python, You can Validate Strings, Numbers, Objects, Arrays, Dates, URLs, Ip addresses & more. You can even create your custom validators! Make sure to give it a ⭐ on github.

XenValidator

XenValidator is a powerful, flexible, and easy-to-use Python library for validating data structures. It simplifies the process of defining complex validation rules, handling cross-field dependencies, and providing informative error messages.

Features

Comprehensive Validation: Supports validation for strings, numbers, dates, arrays, urls, and custom data types.

Custom Error Handling: Easily customize error messages for various validation rules.

Flexible Date Formats: Supports multiple date formats to match various use cases.

Installation

Install XenValidator using pip:

    pip install xenvalidator

Example usage:

Import the package into to your app:

    from XenValidator import X, Object, ValidationError

Create and validate the schema.

    Schema = Object({
        "Name": X["STRING"](),
        "Age":  X["NUMBER"]().min(2),
        "Tags": X["ARRAY"](),
        "IsProMember": X["BOOLEAN"](),
        "Auth": Object({
            "Email": X["STRING"]().email().required(),
            "Password": X["Password"]().min(8).max(64).required().message("Password is required and must be 8 characters long."),
            "IpAddress": X["IPADDRESS"]().required().message("Invalid IP address."),
            "JoinedOn": X["DATE"](),
        }),
        "SiteName": X["URL"](),
    })

    try:
        Data = {
            "Name": "John Doe",
            "Age": 18,
            "Tags": ["Live streamer", "Web designer", "Front-end developer"],
            "IsProMember": False,
            "Auth": {
                "Email": "[e-mail]",
                "Password": "SecurePassword123&",
                "IpAddress": "127.0.0.1",
                "JoinedOn": "2024-11-05",
            },
            "SiteName": "https://mysite.com",
        }
        Schema.validate(Data)
        print("Data is valid:", Data)
    except ValidationError as e:
        print("Data is invalid:", e)

Create custom types to validate

    # Define a custom type to validate

    # This is a type which takes a positive number
    def isPositive(value):
        return isinstance(value, (int, float)) and value > 0

    # Define your schema
    Schema = Object({
        "Amount": X["CUSTOM"](isPositive).required().message("Amount must be positive."),
    })

    # Validate the Data
    try:
        Data = {
            "Amount": 10000  # Valid positive number (A negative number should return an error)
        }
        Schema.validate(Data)
        print("Valid amount:", Data)
    except ValidationError as e:
        print("Invalid amount:", e)

Explanation

• Name: Required field validated as a string.
• Age: Number field with a minimum value of 2.
• Tags: Array field validated to ensure it is indeed an array.
• IsProMember: Boolean field indicating if the user is a pro member.
• Auth: Nested object for authentication details.
  • Email: Required field validated as a valid email.
  • Password: Required string with length between 8 and 64 characters.
  • IpAddress: Required field validated as a valid IP address.
  • JoinedOn: Optional date field.
• SiteName: Optional field validated as a URL.