Validator Collection

Python library of 60+ commonly-used validator functions

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The Validator Collection is a Python library that provides more than 60 functions that can be used to validate the type and contents of an input value.

Each function has a consistent syntax for easy use, and has been tested on Python 2.7, 3.4, 3.5, 3.6, 3.7, and 3.8.

For a list of validators available, please see the lists below.

COMPLETE DOCUMENTATION ON READTHEDOCS: http://validator-collection.readthedocs.io/en/latest

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Contents

• Installation
• Available Validators and Checkers
  • Validators
  • Checkers
• Hello, World and Standard Usage
  • Using Validators
    • When Validation Fails
    • Disabling Validation
  • Using Checkers
    • Disabling Checking
• Best Practices
  • Defensive Approach: Check, then Convert if Necessary
  • Confident Approach: try ... except
• Questions and Issues
• Contributing
• Testing
• License

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Installation

To install the Validator Collection, just execute:

$ pip install validator-collection

Dependencies:

Python 3.x	Python 2.7
jsonschema for JSON Schema Validation.	jsonschema for JSON Schema Validation. The regex drop-in replacement for Python's (buggy) standard re module. Conditional dependencies will be automatically installed if you are installing to Python 2.x.

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Available Validators and Checkers

Validators

SEE: Validator Reference

Core	Date/Time	Numbers	File-related	Internet-related
dict	date	numeric	bytesIO	email
json	datetime	integer	stringIO	url
string	time	float	path	domain
iterable	timezone	fraction	path_exists	ip_address
none	timedelta	decimal	file_exists	ipv4
not_empty			directory_exists	ipv6
uuid			readable	mac_address
variable_name			writeable	mimetype
			executable

Checkers

SEE: Checker Reference

Core	Date/Time	Numbers	File-related	Internet-related
is_type	is_date	is_numeric	is_bytesIO	is_email
is_between	is_datetime	is_integer	is_stringIO	is_url
has_length	is_time	is_float	is_pathlike	is_domain
are_equivalent	is_timezone	is_fraction	is_on_filesystem	is_ip_address
are_dicts_equivalent	is_timedelta	is_decimal	is_file	is_ipv4
is_dict			is_directory	is_ipv6
is_json			is_readable	is_mac_address
is_string			is_writeable	is_mimetype
is_iterable			is_executable
is_not_empty
is_none
is_callable
is_uuid
is_variable_name

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Hello, World and Standard Usage

All validator functions have a consistent syntax so that using them is pretty much identical. Here's how it works:

from validator_collection import validators, checkers, errors

email_address = validators.email('test@domain.dev')
# The value of email_address will now be "test@domain.dev"

email_address = validators.email('this-is-an-invalid-email')
# Will raise a ValueError

try:
    email_address = validators.email(None)
    # Will raise an EmptyValueError
except errors.EmptyValueError:
    # Handling logic goes here
except errors.InvalidEmailError:
    # More handlign logic goes here

email_address = validators.email(None, allow_empty = True)
# The value of email_address will now be None

email_address = validators.email('', allow_empty = True)
# The value of email_address will now be None

is_email_address = checkers.is_email('test@domain.dev')
# The value of is_email_address will now be True

is_email_address = checkers.is_email('this-is-an-invalid-email')
# The value of is_email_address will now be False

is_email_address = checkers.is_email(None)
# The value of is_email_address will now be False

Pretty simple, right? Let's break it down just in case: Each validator comes in two flavors: a validator and a checker.

Using Validators

SEE: Validator Reference

A validator does what it says on the tin: It validates that an input value is what you think it should be, and returns its valid form.

Each validator is expressed as the name of the thing being validated, for example email().

Each validator accepts a value as its first argument, and an optional allow_empty boolean as its second argument. For example:

email_address = validators.email(value, allow_empty = True)

If the value you're validating validates successfully, it will be returned. If the value you're validating needs to be coerced to a different type, the validator will try to do that. So for example:

validators.integer(1)
validators.integer('1')

will both return an int of 1.

If the value you're validating is empty/falsey and allow_empty is False, then the validator will raise a EmptyValueError exception (which inherits from the built-in ValueError). If allow_empty is True, then an empty/falsey input value will be converted to a None value.

CAUTION: By default, allow_empty is always set to False.

HINT: Some validators (particularly numeric ones like integer) have additional options which are used to make sure the value meets criteria that you set for it. These options are always included as keyword arguments after the allow_empty argument, and are documented for each validator below.

When Validation Fails

Validators raise exceptions when validation fails. All exceptions raised inherit from built-in exceptions like ValueError, TypeError, and IOError.

If the value you're validating fails its validation for some reason, the validator may raise different exceptions depending on the reason. In most cases, this will be a descendent of ValueError though it can sometimes be a TypeError, or an IOError, etc.

For specifics on each validator's likely exceptions and what can cause them, please review the Validator Reference

HINT: While validators will always raise built-in exceptions from the standard library, to give you greater programmatic control over how to respond when validation fails, we have defined a set of custom exceptions that inherit from those built-ins.

Our custom exceptions provide you with very specific, fine-grained information as to why validation for a given value failed. In general, most validators will raise ValueError or TypeError exceptions, and you can safely catch those and be fine. But if you want to handle specific types of situations with greater control, then you can instead catch EmptyValueError, CannotCoerceError, MaximumValueError, and the like.

For more detailed information, please see:

• Error Reference
• Validator Reference

Disabling Validation

CAUTION: If you are disabling validators using the VALIDATORS_DISABLED environment variable, their related checkers will also be disabled (meaning they will always return True).

Validation can at times be an expensive (in terms of performance) operation. As a result, there are times when you want to disable certain kinds of validation when running in production. Using the Validator-Collection this is simple:

Just add the name of the validator you want disabled to the VALIDATORS_DISABLED environment variable, and validation will automatically be skipped.

CAUTION: VALIDATORS_DISABLED expects a comma-separated list of values. If it isn't comma-separated, it won't work properly.

Here's how it works in practice. Let's say we define the following environment variable:

$ export VALIDATORS_DISABLED = "variable_name, email, ipv4"

This disables the variable_name(), email(), and ipv4() validators respectively.

Now if we run:

from validator_collection import validators, errors

try:
    result = validators.variable_name('this is an invalid variable name')
except ValueError:
    # handle the error

The validator will return the value supplied to it un-changed. So that means result will be equal to this is an invalid variable name.

However, if we run:

from validator_collection import validators, errors

try:
    result = validators.integer('this is an invalid variable name')
except errors.NotAnIntegerError:
    # handle the error

the validator will run and raise NotAnIntegerError.

We can force validators to run (even if disabled using the environment variable) by passing a force_run = True keyword argument. For example:

from validator_collection import validators, errors

try:
    result = validators.variable_name('this is an invalid variable name',
                                      force_run = True)
except ValueError:
    # handle the error

will produce a InvalidVariableNameError (which is a type of ValueError).

Using Checkers

Please see the Checker Reference

Likewise, a checker is what it sounds like: It checks that an input value is what you expect it to be, and tells you True/False whether it is or not.

IMPORTANT: Checkers do not verify or convert object types. You can think of a checker as a tool that tells you whether its corresponding validator would fail. See Best Practices for tips and tricks on using the two together.

Each checker is expressed as the name of the thing being validated, prefixed by is_. So the checker for an email address is is_email() and the checker for an integer is is_integer().

Checkers take the input value you want to check as their first (and often only) positional argumet. If the input value validates, they will return True. Unlike validators, checkers will not raise an exception if validation fails. They will instead return False.

HINT: If you need to know why a given value failed to validate, use the validator instead.

HINT: Some checkers (particularly numeric ones like is_integer()) have additional options which are used to make sure the value meets criteria that you set for it. These options are always optional and are included as keyword arguments after the input value argument. For details, please see the Checker Reference.

Disabling Checking

CAUTION: If you are disabling validators using the VALIDATORS_DISABLED environment variable, their related checkers will also be disabled. This means they will always return True unless called with force_run = True.

Checking can at times be an expensive (in terms of performance) operation. As a result, there are times when you want to disable certain kinds of checking when running in production. Using the Validator-Collection this is simple:

Just add the name of the checker you want disabled to the CHECKERS_DISABLED environment variable, and validation will automatically be skipped.

CAUTION: CHECKERS_DISABLED expects a comma-separated list of values. If it isn't comma-separated, it won't work properly.

Here's how it works in practice. Let's say we define the following environment variable:

$ export CHECKERS_DISABLED = "is_variable_name, is_email, is_ipv4"

This disables the is_variable_name(), is_email(), and is_ipv4() checkers respectively.

Now if we run:

from validator_collection import checkers

result = checkers.is_variable_name('this is an invalid variable name')
# result will be True

The checker will return True.

However, if we run:

from validator_collection import checkers

result = validators.is_integer('this is an invalid variable name')
# result will be False

the checker will return False

We can force checkers to run (even if disabled using the environment variable) by passing a force_run = True keyword argument. For example:

from validator_collection import checkers

result = checkers.is_variable_name('this is an invalid variable name',
                                   force_run = True)
# result will be False

will return False.

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Best Practices

Checkers and Validators are designed to be used together. You can think of them as a way to quickly and easily verify that a value contains the information you expect, and then make sure that value is in the form your code needs it in.

There are two fundamental patterns that we find work well in practice.

Defensive Approach: Check, then Convert if Necessary

We find this pattern is best used when we don't have any certainty over a given value might contain. It's fundamentally defensive in nature, and applies the following logic:

1. Check whether value contains the information we need it to or can be converted to the form we need it in.
2. If value does not contain what we need but can be converted to what we need, do the conversion.
3. If value does not contain what we need but cannot be converted to what we need, raise an error (or handle it however it needs to be handled).

We tend to use this where we're first receiving data from outside of our control, so when we get data from a user, from the internet, from a third-party API, etc.

Here's a quick example of how that might look in code:

from validator_collection import checkers, validators

def some_function(value):
    # Check whether value contains a whole number.
    is_valid = checkers.is_integer(value,
                                   coerce_value = False)

    # If the value does not contain a whole number, maybe it contains a
    # numeric value that can be rounded up to a whole number.
    if not is_valid and checkers.is_integer(value, coerce_value = True):
        # If the value can be rounded up to a whole number, then do so:
        value = validators.integer(value, coerce_value = True)
    elif not is_valid:
        # Since the value does not contain a whole number and cannot be converted to
        # one, this is where your code to handle that error goes.
        raise ValueError('something went wrong!')

    return value

value = some_function(3.14)
# value will now be 4

new_value = some_function('not-a-number')
# will raise ValueError

Let's break down what this code does. First, we define some_function() which takes a value. This function uses the is_integer() checker to see if value contains a whole number, regardless of its type.

If it doesn't contain a whole number, maybe it contains a numeric value that can be rounded up to a whole number? It again uses the is_integer() to check if that's possible. If it is, then it calls the integer() validator to coerce value to a whole number.

If it can't coerce value to a whole number? It raises a ValueError.

Confident Approach: try ... except

Sometimes, we'll have more confidence in the values that we can expect to work with. This means that we might expect value to generally have the kind of data we need to work with. This means that situations where value doesn't contain what we need will truly be exceptional situations, and can be handled accordingly.

In this situation, a good approach is to apply the following logic:

1. Skip a checker entirely, and just wrap the validator in a try...except block.

We tend to use this in situations where we're working with data that our own code has produced (meaning we know - generally - what we can expect, unless something went seriously wrong).

Here's an example:

from validator_collection import validators, errors

def some_function(value):
    try:
        email_address = validators.email(value, allow_empty = False)
    except errors.InvalidEmailError as error:
        # handle the error here
    except ValueError as error:
        # handle other ValueErrors here

    # do something with your new email address value

    return email_address

email = some_function('email@domain.com')
# This will return the email address.

email = some_function('not-a-valid-email')
# This will raise a ValueError that some_function() will handle.

email = some_function(None)
# This will raise a ValueError that some_function() will handle.

So what's this code do? It's pretty straightforward. some_function() expects to receive a value that contains an email address. We expect that value will typically be an email address, and not something weird (like a number or something). So we just try the validator - and if validation fails, we handle the error appropriately.

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Questions and Issues

You can ask questions and report issues on the project's Github Issues Page

Contributing

We welcome contributions and pull requests! For more information, please see the Contributor Guide.

And thanks to all those who have contributed!

Testing

We use TravisCI for our build automation and ReadTheDocs for our documentation.

Detailed information about our test suite and how to run tests locally can be found in our Testing Reference.

License

The Validator Collection is made available on a MIT License.