Form and field validation

    In general, any cleaning method can raise ValidationError if there is a problem with the data it is processing, passing the relevant information to the ValidationError constructor. See below for the best practice in raising ValidationError. If no ValidationError is raised, the method should return the cleaned (normalized) data as a Python object.

    Most validation can be done using - helpers that can be reused. Validators are functions (or callables) that take a single argument and raise ValidationError on invalid input. Validators are run after the field’s to_python and validate methods have been called.

    Validation of a form is split into several steps, which can be customized or overridden:

    • The to_python() method on a Field is the first step in every validation. It coerces the value to a correct datatype and raises ValidationError if that is not possible. This method accepts the raw value from the widget and returns the converted value. For example, a FloatField will turn the data into a Python float or raise a ValidationError.

    • The validate() method on a Field handles field-specific validation that is not suitable for a validator. It takes a value that has been coerced to a correct datatype and raises ValidationError on any error. This method does not return anything and shouldn’t alter the value. You should override it to handle validation logic that you can’t or don’t want to put in a validator.

    • The run_validators() method on a Field runs all of the field’s validators and aggregates all the errors into a single ValidationError. You shouldn’t need to override this method.

    • The clean() method on a Field subclass is responsible for running to_python(), validate(), and run_validators() in the correct order and propagating their errors. If, at any time, any of the methods raise ValidationError, the validation stops and that error is raised. This method returns the clean data, which is then inserted into the cleaned_data dictionary of the form.

    • The clean_<fieldname>() method is called on a form subclass – where <fieldname> is replaced with the name of the form field attribute. This method does any cleaning that is specific to that particular attribute, unrelated to the type of field that it is. This method is not passed any parameters. You will need to look up the value of the field in self.cleaned_data and remember that it will be a Python object at this point, not the original string submitted in the form (it will be in cleaned_data because the general field clean() method, above, has already cleaned the data once).

      For example, if you wanted to validate that the contents of a CharField called serialnumber was unique, clean_serialnumber() would be the right place to do this. You don’t need a specific field (it’s a CharField), but you want a formfield-specific piece of validation and, possibly, cleaning/normalizing the data.

      The return value of this method replaces the existing value in cleaned_data, so it must be the field’s value from cleaned_data (even if this method didn’t change it) or a new cleaned value.

    • The form subclass’s clean() method can perform validation that requires access to multiple form fields. This is where you might put in checks such as “if field A is supplied, field B must contain a valid email address”. This method can return a completely different dictionary if it wishes, which will be used as the cleaned_data.

      Since the field validation methods have been run by the time clean() is called, you also have access to the form’s errors attribute which contains all the errors raised by cleaning of individual fields.

      Note that any errors raised by your Form.clean() override will not be associated with any field in particular. They go into a special “field” (called __all__), which you can access via the method if you need to. If you want to attach errors to a specific field in the form, you need to call add_error().

      Also note that there are special considerations when overriding the clean() method of a ModelForm subclass. (see the for more information)

    Examples of each of these methods are provided below.

    As mentioned, any of these methods can raise a ValidationError. For any field, if the Field.clean() method raises a ValidationError, any field-specific cleaning method is not called. However, the cleaning methods for all remaining fields are still executed.

    In order to make error messages flexible and easy to override, consider the following guidelines:

    • Provide a descriptive error code to the constructor:

    • Don’t coerce variables into the message; use placeholders and the params argument of the constructor:

      1. # Good
      2. ValidationError(
      3.     _('Invalid value: %(value)s'),
      4.     params={'value': '42'},
      5. )
      7. # Bad

    • Use mapping keys instead of positional formatting. This enables putting the variables in any order or omitting them altogether when rewriting the message:

      1. # Good
      2. ValidationError(
      3.     _('Invalid value: %(value)s'),
      4.     params={'value': '42'},
      5. )
      7. # Bad
      8. ValidationError(
      9.     _('Invalid value: %s'),
      10.     params=('42',),
      11. )

    Putting it all together:

    1. raise ValidationError(
    2.     _('Invalid value: %(value)s'),
    3.     code='invalid',
    4.     params={'value': '42'},
    5. )

    Following these guidelines is particularly necessary if you write reusable forms, form fields, and model fields.

    While not recommended, if you are at the end of the validation chain (i.e. your form clean() method) and you know you will never need to override your error message you can still opt for the less verbose:

    The Form.errors.as_data() and methods greatly benefit from fully featured ValidationErrors (with a code name and a params dictionary).

    If you detect multiple errors during a cleaning method and wish to signal all of them to the form submitter, it is possible to pass a list of errors to the ValidationError constructor.

    As above, it is recommended to pass a list of ValidationError instances with codes and params but a list of strings will also work:

    1. # Good
    2. raise ValidationError([
    3.     ValidationError(_('Error 1'), code='error1'),
    4.     ValidationError(_('Error 2'), code='error2'),
    5. ])
    7. # Bad
    8. raise ValidationError([
    9.     _('Error 1'),
    10.     _('Error 2'),
    11. ])

    Using validation in practice

    The previous sections explained how validation works in general for forms. Since it can sometimes be easier to put things into place by seeing each feature in use, here are a series of small examples that use each of the previous features.

    Validators can be used to validate values inside the field, let’s have a look at Django’s SlugField:

    1. from django.core import validators
    2. from django.forms import CharField
    4. class SlugField(CharField):
    5.     default_validators = [validators.validate_slug]

    As you can see, SlugField is a CharField with a customized validator that validates that submitted text obeys to some character rules. This can also be done on field definition so:

    1. slug = forms.SlugField()

    is equivalent to:

    1. slug = forms.CharField(validators=[validators.validate_slug])

    Common cases such as validating against an email or a regular expression can be handled using existing validator classes available in Django. For example, validators.validate_slug is an instance of a constructed with the first argument being the pattern: ^[-a-zA-Z0-9_]+$. See the section on writing validators to see a list of what is already available and for an example of how to write a validator.

    Let’s first create a custom form field that validates its input is a string containing comma-separated email addresses. The full class looks like this:

    Every form that uses this field will have these methods run before anything else can be done with the field’s data. This is cleaning that is specific to this type of field, regardless of how it is subsequently used.

    Let’s create a ContactForm to demonstrate how you’d use this field:

    1. class ContactForm(forms.Form):
    2.     subject = forms.CharField(max_length=100)
    3.     message = forms.CharField()
    4.     sender = forms.EmailField()
    5.     recipients = MultiEmailField()
    6.     cc_myself = forms.BooleanField(required=False)

    Use MultiEmailField like any other form field. When the is_valid() method is called on the form, the method will be run as part of the cleaning process and it will, in turn, call the custom to_python() and validate() methods.

    Continuing on from the previous example, suppose that in our ContactForm, we want to make sure that the recipients field always contains the address "fred@example.com". This is validation that is specific to our form, so we don’t want to put it into the general MultiEmailField class. Instead, we write a cleaning method that operates on the recipients field, like so:

    1. from django import forms
    3. class ContactForm(forms.Form):
    4.     # Everything as before.
    5.     ...
    7.     def clean_recipients(self):
    8.         data = self.cleaned_data['recipients']
    9.         if "fred@example.com" not in data:
    10.             raise ValidationError("You have forgotten about Fred!")
    12.         # Always return a value to use as the new cleaned data, even if
    13.         # this method didn't change it.
    14.         return data

    Suppose we add another requirement to our contact form: if the cc_myself field is True, the subject must contain the word "help". We are performing validation on more than one field at a time, so the form’s method is a good spot to do this. Notice that we are talking about the clean() method on the form here, whereas earlier we were writing a clean() method on a field. It’s important to keep the field and form difference clear when working out where to validate things. Fields are single data points, forms are a collection of fields.

    By the time the form’s clean() method is called, all the individual field clean methods will have been run (the previous two sections), so self.cleaned_data will be populated with any data that has survived so far. So you also need to remember to allow for the fact that the fields you are wanting to validate might not have survived the initial individual field checks.

    There are two ways to report any errors from this step. Probably the most common method is to display the error at the top of the form. To create such an error, you can raise a ValidationError from the clean() method. For example:

    1. from django import forms
    2. from django.core.exceptions import ValidationError
    4. class ContactForm(forms.Form):
    5.     # Everything as before.
    6.     ...
    8.     def clean(self):
    9.         cleaned_data = super().clean()
    10.         cc_myself = cleaned_data.get("cc_myself")
    11.         subject = cleaned_data.get("subject")
    13.         if cc_myself and subject:
    14.             # Only do something if both fields are valid so far.
    15.             if "help" not in subject:
    16.                 raise ValidationError(
    17.                     "Did not send for 'help' in the subject despite "
    18.                     "CC'ing yourself."
    19.                 )

    In this code, if the validation error is raised, the form will display an error message at the top of the form (normally) describing the problem. Such errors are non-field errors, which are displayed in the template with {{ form.non_field_errors }}.

    The call to super().clean() in the example code ensures that any validation logic in parent classes is maintained. If your form inherits another that doesn’t return a cleaned_data dictionary in its clean() method (doing so is optional), then don’t assign cleaned_data to the result of the super() call and use self.cleaned_data instead:

    1. def clean(self):
    2.     super().clean()
    3.     cc_myself = self.cleaned_data.get("cc_myself")

    The second argument of add_error() can be a string, or preferably an instance of ValidationError. See Raising ValidationError for more details. Note that automatically removes the field from cleaned_data.