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https://github.com/insightindustry/validator-collection
Python library of 60+ commonly-used validator functions
https://github.com/insightindustry/validator-collection
python-library validation validation-library validators
Last synced: 2 months ago
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Python library of 60+ commonly-used validator functions
- Host: GitHub
- URL: https://github.com/insightindustry/validator-collection
- Owner: insightindustry
- License: mit
- Created: 2018-04-14T14:42:54.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T00:58:24.000Z (about 2 years ago)
- Last Synced: 2024-05-28T12:57:34.280Z (7 months ago)
- Topics: python-library, validation, validation-library, validators
- Language: Python
- Homepage: http://validator-collection.readthedocs.io/en/latest/index.html
- Size: 404 KB
- Stars: 123
- Watchers: 3
- Forks: 11
- Open Issues: 15
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES.rst
- Contributing: docs/contributing.rst
- License: LICENSE
Awesome Lists containing this project
README
======================
Validator Collection
======================
**Python library of 60+ commonly-used validator functions**
.. list-table::
:widths: 10 90
:header-rows: 1
* - Branch
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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:**
.. list-table::
:widths: 50 50
:header-rows: 1
* - 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.
-------
***********************************
Available Validators and Checkers
***********************************
Validators
=============
**SEE:** `Validator Reference `_
.. list-table::
:widths: 30 30 30 30 30
:header-rows: 1
* - 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 `_
.. list-table::
:widths: 30 30 30 30 30
:header-rows: 1
* - 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``
-
-
-
-
-----
************************************
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:
.. code-block:: python
from validator_collection import validators, checkers, errors
email_address = validators.email('[email protected]')
# The value of email_address will now be "[email protected]"
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('[email protected]')
# 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.
.. _validators-explained:
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:
.. 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.
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 <#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:
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 <#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 `_.
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:
.. 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')
# result will be True
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')
# 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:
.. code-block:: python
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``.
.. _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.
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:
#. 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)
# 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:
#. 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 protected]')
# 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 `_.
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**.