Collection of 60+ Python functions for validating data
Python library of 60+ commonly-used validator functions
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latest <https://github.com/insightindustry/validator-collection/tree/master>_
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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
.. contents:: Contents :local: :depth: 3 :backlinks: entry
Installation
To install the Validator Collection, just execute:
.. code:: bash
$ pip install validator-collection
Dependencies:
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jsonschema <https://pypi.org/project/jsonschema/>_ for JSON Schema Validation.
jsonschema <https://pypi.org/project/jsonschema/>_ for JSON Schema Validation.
The regex <https://pypi.python.org/pypi/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
SEE: Validator Reference <http://validator-collection.readthedocs.io/en/latest/validators.html>_
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dictdatenumericbytesIOemailjsondatetimeintegerstringIOurlstringtimefloatpathdomainiterabletimezonefractionpath_existsip_addressnonetimedeltadecimalfile_existsipv4not_emptydirectory_existsipv6uuidreadablemac_addressvariable_namewriteablemimetypeexecutableSEE: Checker Reference <http://validator-collection.readthedocs.io/en/latest/checkers.html>_
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is_typeis_dateis_numericis_bytesIOis_emailis_betweenis_datetimeis_integeris_stringIOis_urlhas_lengthis_timeis_floatis_pathlikeis_domainare_equivalentis_timezoneis_fractionis_on_filesystemis_ip_addressare_dicts_equivalentis_timedeltais_decimalis_fileis_ipv4is_dictis_directoryis_ipv6is_jsonis_readableis_mac_addressis_stringis_writeableis_mimetypeis_iterableis_executableis_not_emptyis_noneis_callableis_uuidis_variable_nameHello, World and Standard Usage
All validator functions have a consistent syntax so that using them is pretty much identical. Here's how it works:
.. code-block:: python
from validator_collection import validators, checkers, errors
email_address = validators.email('test@domain.dev')
email_address = validators.email('this-is-an-invalid-email')
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)
email_address = validators.email('', allow_empty = True)
is_email_address = checkers.is_email('test@domain.dev')
is_email_address = checkers.is_email('this-is-an-invalid-email')
is_email_address = checkers.is_email(None)
Pretty simple, right? Let's break it down just in case: Each validator comes in two flavors: a validator and a checker.
.. _validators-explained:
SEE: Validator Reference <http://validator-collection.readthedocs.io/en/latest/validators.html>_
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:
.. code-block:: python
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:
.. code-block:: python
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.
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 <http://validator-collection.readthedocs.io/en/latest/validators.html>_
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 <http://validator-collection.readthedocs.io/en/latest/errors.html>_Validator Reference <http://validator-collection.readthedocs.io/en/latest/validators.html>_CAUTION: If you are disabling validators <#disabling-validation>_ 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:
.. code-block:: bash
$ export VALIDATORS_DISABLED = "variable_name, email, ipv4"
This disables the variable_name(), email(), and ipv4() validators respectively.
Now if we run:
.. code-block:: python
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:
.. code-block:: python
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:
.. code-block:: python
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).
.. _checkers-explained:
Please see the Checker Reference <http://validator-collection.readthedocs.io/en/latest/checkers.html>_
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 <#using-validators>_
would fail. See Best Practices <#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 <#using-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 <http://validator-collection.readthedocs.io/en/latest/checkers.html>_.
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:
.. code-block:: bash
$ 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:
.. code-block:: python
from validator_collection import checkers
result = checkers.is_variable_name('this is an invalid variable name')
The checker will return True.
However, if we run:
.. code-block:: python
from validator_collection import checkers
result = validators.is_integer('this is an invalid variable name')
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:
.. code-block:: python
from validator_collection import checkers
result = checkers.is_variable_name('this is an invalid variable name', force_run = True)
will return False.
.. _best-practices:
Best Practices
Checkers <#using-checkers>_ and Validators <#using-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.
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:
#. Check whether value contains the information we need it to or can be
converted to the form we need it in.
#. If value does not contain what we need but can be converted to what
we need, do the conversion.
#. 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:
.. code-block:: python
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)
new_value = some_function('not-a-number')
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.
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:
#. 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:
.. code-block:: python
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')
email = some_function('not-a-valid-email')
email = some_function(None)
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 <https://github.com/insightindustry/validator-collection/issues>_
Contributing
We welcome contributions and pull requests! For more information, please see the
Contributor Guide <http://validator-collection.readthedocs.io/en/latest/contributing.html>_.
And thanks to all those who have contributed <https://github.com/insightindustry/validator-collection/graphs/contributors>_!
Testing
We use TravisCI <http://travisci.org>_ for our build automation and
ReadTheDocs <https://readthedocs.org>_ for our documentation.
Detailed information about our test suite and how to run tests locally can be
found in our Testing Reference <http://validator-collection.readthedocs.io/en/latest/testing.html>_.
License
The Validator Collection is made available on a MIT License.