New in version 0.4

Validations are used to ensure that only valid data is parsed and/or stored from forms and user interactions. For example, it may be important to your application to ensure that every user provides a valid email address, or that an input field contains a valid number, or a date with the right format.

The validation mechanism in weppy is the same both if you're using independent forms or model's ones, and is built to be easy to use, provides built-in helpers for common needs, but also allows you to create your own validation methods as well.

Validations cannot be bypassed by end users, and happens every time a form is submitted, or when you invoke the Model methods validate and create. But how you define validations for your entities and forms?

The quite immediate way is to use the validation parameter of the Field class which is used both by Model and Form classes in weppy, and that accepts a dict of instructions:

# ensure the input is a valid email address
Field(validation={'is': 'email'})

And when you're using models, you may find yourself more comfortable in grouping all fields validations into a single place, with the validation attribute of your Model class:

class Person(Model):
    email = Field()
    website = Field()

    validation = {
        'email': {'is': 'email'},
        'website': {'is': 'url', 'message': 'You must insert a valid url'}

The result of using Field's validation parameter or Model's validation attribute will be the same: is basically a matter of personal coding style.

As you've seen with the website field in the example, you can always customize the error message resulting from the validation.

Now, let's see the available built-in validation helpers.

Presence and absence of input

weppy provides two helpers when you just need to ensure that a specific field is not blank or is blank: 'presence' and 'empty'.

In fact, when you need to ensure a field is not empty, you can use the 'presence' validator:

myfield = Field(validation={'presence': True})

In this case, the presence validator ensure that the contents of the input are a valid, so a blank input or some white spaces won't pass the validation.

On the contrary, if you need to ensure your field is blank, you can use the 'emtpy' validator:

myfield = Field(validation={'empty': True})

If you prefer, you may also write:

myfield = Field(validation={'empty': False})

which will be the same of {'presence': True}, but remember that writing {'presence': False} doesn't mean {'empty': True}.

When you're applying the 'presence' validator to a reference field, the behavior of the validator will be quite different, since it will also check that the given value is existent in the referenced table.

Type of input

In the first examples, we ensured that the input values were emails or urls. This is done with the 'is' validator, and can be used to ensure several types for your fields:

is values validation details
int ensure int type
float ensure float type
decimal ensure decimal.Decimal type
date ensure type
time ensure datetime.time type
datetime ensure datetime.datetime type
email ensure is a valid email address
url ensure is a valid url
ip ensure is a valid ip
json ensure is valid json content
image (for upload fields) ensure the input is an image file
list:type ensure is a list with elements of given type (available with all 'is' values except for image and json)

Since many options of the 'is' validator ensure a specific python type, on validation the input values will also be converted to the right type: an input which should be 'int' and that it comes as a string from the form, will be converted as an int object for all the other validators, or for your post-validation code.

Here are some examples of 'is' validation helper:

price = Field('float', validation={'is': {'float': {'dot': ','}}})
emails = Field('list:string', validation={'is': {'list:email': {'splitter': ';'}}})
urls = Field('list:string', validation={'is': 'list:url'})

Note that, since 'is' validator ensure the input is valid for the given type, it's like an implicit {'presence': True}, so you don't need to add 'presence' when you use it.

– Dude what if I want the field can be blank, and when it's not, of a specific type?
you can use the 'allow' validator described next

Also remember that Field comes with a default 'is' validator (unless you disabled it with the auto_validation parameter) depending on its type (so an int field will have an {'is': 'int'} validator, since weppy guess you want the input to be valid) as we described in the specific chapter.

Specific values allowance

In the previous paragraph we saw that 'is' validator implicitly consist also in a {'presence': True} validation. Now, what if we need to allow an int field to be blank, so that when is filled it will be converted to an integer and when it's not, it will pass the validation anyway?

We can use the 'allow' validator:

maybe_number = Field('int', validation={'allow': 'blank'})
# or
maybe_number = Field('int', validation={'allow': 'empty'})

In this specific case we are telling the validator to accept blank/empty inputs, but you can also pass specific values to it:

maybe_number = Field('int', validation={'allow': None})
maybe_number = Field('int', validation={'allow': 'nope'})

Practically speaking, 'allow' validator allows you to add an exception rule to your validation, and can be applied to anyone of the validators described in this page.

Length of input

You will need to set some boundaries on the length of your fields. A very common scenario will be a password field, that you want to be greater than a certain a length and/or not too long:

password = Field('password', validation={'len': {'gt': 5, 'lt': 25}})

As you can see, the 'len' validator is just the easier way to do that. It accepts different arguments, depending on what you need:

parameter value expected example
gt int {'len': {'gt': 5}}
lt int {'len': {'lt': 25}}
gte int {'len': {'gte': 6}}
lte int {'len': {'lte': 24}}
range tuple of int {'len': {'range': (6, 25)}}

Note that the range parameter behaves like the python builtin range(), including the lower value and excluding the upper one, so the above line is the same of {'len': {'gte': 6, 'lt': 25}}.

Value inclusion

To ensure the value is inside a specific set, you can use the 'in' validator:

myfield = Field(validation={'in': ['a', 'b']})

and if you have an int field, you can also use the convenient 'range' option:

number = Field('int', validation={'in': {'range': (1, 10)}})

The 'in' validator also accepts some specific options, in particular:

parameter description
labels a list of values to display on form's dropdown
multiple allow user to select multiple values

When you want to use these options with a set, you may use the 'set' notation:

number = Field(
    validation={'in': {'set': [0, 1], 'labels': ['zero', 'one']}}

Numeric boundaries

When you need to ensure some numeric values on int fields, you may find useful a different approach from {'in': {'range': (1, 10)}}, using the same notation of 'len':

num_a = Field('int', validation={'gt': 0})
num_b = Field('int', validation={'lt': 12})
num_c = Field('int', validation={'gte': 1, 'lte': 10})

Validate only a specific value

Sometimes you need to validate only a specific value for a field. weppy provides the 'equals' validator to help you:

accept_terms = Field(validation={'equals': 'yes'})

Basically the 'equals' validator perform a == check between the input value and the one given.

Match input

When you want to validate a match of a regex expression, you can use the 'match' validation helper. For example, let say you want to validate a ZIP code:

zip = Field(validation={'match': '^\d{5}(-\d{4})?$'})

or a phone number:

phone = Field(validation={'match': '^1?((-)\d{3}-?|\(\d{3}\))\d{3}-?\d{4}$'})

'match' also accepts some parameters:

  • the search parameter (default to False), which will use the regex method search instead of the match one
  • the strict parameter (default to False), which will only matches the beginning of the string.

In this example, due to the strict parameter, the value for the first field will pass validation and the second won't:

normal = Field(validation={'match': 'ab'})
strict = Field(validation={'match': {'expression': 'ab', 'strict': True}})
Model.validate({'normal': 'abc', 'strict': 'abc'})


When you need to do an exclusion operation under validation process, you can use the 'not' validation helper. Let's say that you want the value to be different from a certain value:

myfield = Field(validation={'not': {'equals': 'somevalue'}})

or you want to exclude a set of values:

color = Field(validation={'not': {'in': ['white', 'black']}})

Basically the 'not' validator takes another validation as argument and check the opposite result.


Sometimes you need to validate an input value that respond to any of the given validations. Under this circumstances, you can use the 'any' validation helper:

myfield = Field(validation={'any': {'is': 'email', 'in': ['foo', 'bar']}})

Obviously the 'any' validator takes other validations as argument and validate the value if any of the children validations pass.


weppy provides several validation helpers that let you transform the input value of the field on validation. For example, you may want your string field to always being lowercase, or you need your password field to be crypted.

Here is the complete list of transformation helpers available in weppy builtins.


The 'lower' helper turns your string to lowercase:

low = Field(validation={'lower': True})


The 'upper' helper is the opposite of the 'lower' one, and will turn your string to uppercase:

up = Field(validation={'upper': True})


The 'clean' helper removes any special character from your string:

clean = Field(validation={'clean': True})


The 'urlify' helper allows you to create url valid strings (so that, for example, you can use them for routing purposes):

urldata = Field(validation={'urlify': True})

This helper also accepts the underscore parameter (default set as False) that you can use if you want underscores to be kept in the string:

urldata = Field(validation={'urlify': {'underscore': True}})


The 'crypt' helper becomes handy when you want to crypt the contents of the field. The easiest way to use it is just to enable it:

password = Field(validation={'crypt': True})

which will crypt the contents using sha512 algorithm.

If you just want to use a different algorithm, choosing between md5, sha1, sha224, sha256, sha384, sha512, you can just write:

password = Field(validation={'crypt': 'md5'})

But 'crypt' also accepts two more parameters:

  • the key parameter, which allows you to specify your own key to use with the algorithm
  • the salt parameter, which allows you to specify a salt to hash the password with.
password = Field(
    validation={'crypt': {'algorithm': 'md5', 'key': 'MyVerySecretKey'}}

Custom validation

Of course the builtin validation helpers cannot be enough in many particular cases. When you need to implement your own validation logic on a specific field, you can create your own Validator subclass, and pass an instance of it to the validation parameter:

from weppy.validators import Validator

class MyValidator(Validator):
    message = "Invalid value"

    def __call__(self, value):
        if value == "notallowed":
            return value, self.message
        return value, None

myfield = Field(validation=MyValidator())

and if you need to use multiple Validator classes, you can pass to validation a list of instances:

myfield = Field(validation=[MyValidator1(), MyValidator2()])

When you write down your own Validator you just have to remember that the validation logic has to be inside the __call__ method, and that should return the value and the error message if validation has failed or the value and None if everything was ok.

Validator class also have a formatter function, that allows you to format the value to display in the form for particular cases:

class FloatValidator(Validator):
    def formatter(self, value):
        #: shows only 2 values after the separator
        if value is None:
            return None
        val = str(value)
        if '.' not in val:
            val += '.00'
            val += '0' * (2 - len(val.split('.')[1]))
        return val