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Collection of 60+ Python functions for validating data

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Python library of 60+ commonly-used validator functions

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v. 1.0.0

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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, and 3.6.

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

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



Installation

To install the Validator Collection, just execute:

$ pip install validator-collection

Dependencies:

Python 3.x

Python 2.7

None. Uses the standard library.

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.


Available Validators and Checkers

Validators

SEE: Validator Reference

Core

Date/Time

Numbers

File-related

Internet-related

dict

date

numeric

bytesIO

email

string

datetime

integer

stringIO

url

iterable

time

float

path

ip_address

none

timezone

fraction

path_exists

ipv4

not_empty

decimal

file_exists

ipv6

uuid

directory_exists

mac_address

variable_name

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_ip_address

are_equivalent

is_timezone

is_fraction

is_on_filesystem

is_ipv4

are_dicts_equivalent

is_decimal

is_file

is_ipv6

is_dict

is_directory

is_mac_address

is_string

is_iterable

is_not_empty

is_none

is_callable

is_uuid

is_variable_name


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

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

email_address = validators.email(None)
# Will raise a ValueError

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 ValueError exception. 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.

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 ValueError though it can sometimes be a TypeError, or an AttributeError, etc. For specifics on each validator’s likely exceptions and what can cause them, please review the Validator Reference.

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.

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.


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

def some_function(value):
    try:
      email_address = validators.email(value, allow_empty = False)
    except ValueError:
      # handle the error 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.


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

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.

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