Validator
Validator is a Python library for dealing with request validating.
Table of Contents
Installation
Use the package manager pip to install Validator.
pip install validatorUsage
User should pass request dictionary and rules dictionary for validating data in the request.
Please see examples below:
from validator import validate
request = {"name": "John Doe",
"age": 33,
"mail": "[email protected]"}
rules = {"name": "required",
"age": "integer|min:18",
"mail": "required|mail"}
result = validate(request, rules) # Truevalidate() returns either True or False.
Another option is to use Validator class
from validator import Validator
request = {...}
rules = {...}
val = Validator(request, rules)
result = val.validate() # TrueValidated Data/Error Messages
Validator allows user to have a look at failed validations and passed validations. validated_data is extremly useful when request contains data that is not needed for initialization of model, you can get rid of them and validate at the same time. See examples below:
-
Validated Data
from validator import validate request = {"first_name": "John", "last_name": "Doe", "age": 33, "mail": "[email protected]", "_token": "WpH0UPfy0AXzMtK2UWtJ", "_cookie_data": "e9Uixp8hzUySy6bw3MuZ", "_session_id": "ZB7q7uIVdWBKgSCSSWAa"} rule = {"first_name": "required", "last_name": "required", "age": "required|min:18", "mail": "required|mail"} result, validated_data, _ = validate(request, rule, return_info=True) """ result = True validated_data = {"first_name": "John", "last_name": "Doe", "age": 33, "mail": "[email protected]"} """
-
Error Messages
from validator import validate request = {"name": "", "mail": "john_doe"} rule = {"name": "required", "mail": "mail"} result, _, errors = validate(request, rule, return_info=True) """ result = False errors = {"name": {"Required': "Field was empty"}, "mail": {"Mail': "Expected a Mail, Got: john_doe"}} """
Or you can use Validator class for error messages as well as for validated data.
val = Validator(request, rules)
result = val.validate()
validated_data = val.get_validated_data()
errors = val.get_errors()Validating Arrays
Validator comes with validate_many() function, which validates multiple requests. Function takes list of requests and one rule. This rule is checked for all the requests. If one or more requests fail validation function returns False, otherwise (if all pass) True. For more details see example below:
Validation Passes:
from validator import validate_many
requests = [{"name": "John"},
{"name": "Rob"},
{"name": "Tom"},
{"name": "Greg"}]
rule = {"name": "required|min:3"}
result = validate_many(requests, rule) # TrueWe can also have a look at failed validations and error messages. validate_many() takes third argument as boolean, indicating return of error messages.
Validation Fails:
from validator import validate_many
requests = [{"name": "John"},
{"name": ""},
{"name": "Yo"},
{"name": "Greg"}]
rule = {"name": "required|min:3"}
result, errors = validate_many(requests, rule, return_info=True)
"""
result = False
errors = [{},
{"name": {"Min": "Expected Maximum: 3, Got: 0", "Required": "Field was empty"}},
{"name": {"Min": "Expected Maximum: 3, Got: 2"}},
{}]
"""Available Validation Rules
Validator comes with pre initialized rules. All of rules are listed in RULES.md file
Rules
Validator Rules can be used in different ways. Please see some examples below:
Strings
rule = {"name": "required",
"age": "integer|min:18",
"mail": "required|mail"}Array of Strings
rule = {"name": ["required"],
"age": ["integer", "min:18"],
"mail": ["required", "mail"]}Array of Rules
from validator import rules as R
rules = {"name": [R.Required()],
"age": [R.Integer(), R.Min(18)],
"mail": [R.Requried(), R.Mail()]}Other Miscellaneous
from validator import rules as R
rules = {"name": R.Required(), # no need for Array Brackets if one rule
"age": [R.Integer, R.Min(18)],
"mail": [R.Requried, R.Mail]} # no need for class initialization with brakcets ()
# if no arguments are passed to ruleRules Interconnection
Rules can affect each other. Let's take a look at Size rule. It takes 1 argument and checks if data is equal to given value (example: 'size:10').
- Case 1: checks for length of '18' to be 18. len('18') is 2, therefore it is False.
request = {"age" : "18"}
rule = {"age" : "size:18"}
validate(request, rule)
"""
result = False
errors = {"age": {"Size": "Expected Size:18, Got:2"}}
"""- Case 2: checks if int representation of '18' is equal to 18. (int('18') = 18), therefore it is True.
request = {"age" : "18"}
rule = {"age" : "integer|size:18"}
validate(request, rule) # TrueFor more details please view Size Rule
Custom Rules
We give users ability to advance and use their own checkers. Write function and use is as a rule. See examples below:
- Use defined functions:
from validator import validate def func_age(x): return x >= 18 req = {"age": 30} rules = {"age": func_age} validate(req, rules)
- Use Lambda functions:
from validator import validate req = {"age": 30} rules = {"age": lambda x: x >= 18} validate(req, rules)
- Any callable class (NOTE: Pass class instance and not class itself):
from validator import validate class checker: def __init__(self): pass def __call__(self, x): return x >= 456 req = {"age": 30} rules = {"age": checker()} validate(req, rules)
- Custom Rule:
from validator import validate from validator.rules import Rule class AgeRule(Rule): def __init__(self, min): Rule.__init__(self) self.min = min def check(self, arg): return self.min <= arg req = {"age": 30} rules = {"age": AgeRule(18)} validate(req, rules)
Examples
We have written some examples for you to get started easier. Please view Examples folder, where you can find validator usages with frameworks like Flask, Django and etc.
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please see CONTRIBUTING.md before making PR :)