The Voluptuous Python library is a python data validation toolkit. Voluptuous in Python can be used to validate different types of data, such as JSON and XML. Voluptuous has the ability to describe complex structures of nested dictionaries with ease.
This makes it possible for developers to quickly validate the input parameters supplied by users into their application code without any hassle at all! Voluptuous is a simple, pure Python library for validation of data structures.
Voluptuous can be used to validate input parameters provided by the user before they are passed on to some function or method in your code. Voluptuous works with nested data types such as maps and arrays/lists which makes it an ideal selection for validating complex JSON inputs supplied by users into mobile apps built using micro-service architecture.
Table of Contents
Data Validation: Check the Accuracy of Your Data Before You Go Ahead and Process It
Data validation is a very important step, but often overlooked. Data validation means checking the accuracy and quality of source data before using it in complicated calculations or for other purposes. You can perform different types of data validations depending on your destination constraints or objectives.
For example, you might want to validate python script input parameters to ensure that they are within certain bounds prior to executing the python script. Python provides the built-in library data validator which makes this process easier!
Data validation is a form of data cleansing. Data cleansing means verifying, correcting and transforming the data before you use it in python scripts or other applications. It’s also sometimes called “data scrubbing” because you are cleaning up the dirty data to make it presentable for your python script or application to use!
You can perform several types of operations on your source data using python data validator library including checking if specific values meet certain conditions (cross-validation), validating whether input files have all required fields, column counts etc., finding duplicates by performing simple string comparison checks between similar columns within different worksheets/files, filtering out rows without any value at all etc…
What is JSON? A Guide to XML
JSON and XML are two different ways of storing data. JSON is a lightweight data-interchange format that is completely language independent and easy to understand and generate. JSON was designed to carry data, not display it like XML does.
Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format that can be read by humans as well as parsed by machines such as web browsers or other software agents. The design goals for XML focus on simplicity, generality, and usability across the internet.
Voluptuous Python Data Validation Library
Python Voluptuous data validation library is a python based tool used for validating data in JSON, XML and other formats. This article will discuss about Voluptuous Python Data Validation Library with three main goals: simplifying the data, providing support while handling complex data structures and providing useful error messages.
It also helps to accept only that required data and discard the rest. Voluptuous is not only limited to validate data but it’s also helpful in converting between various formats. Voluptuous has both validating and non-validating types of parsers, depending on the use case. It works with Python versions from version (including) all the way up to latest one (excluding).
Why You Should Use Voluptuous in Python: A Validation Library
One of the most overlooked aspects in any programming language is validation. Why? There are many reasons, but the simplest answer would be that it’s boring to write validation code. Why take time away from doing what you love to do—making your app awesome—to validate data? But if you don’t do it, then errors will happen and users will get frustrated.
Validation libraries are a way for developers to save themselves time while also providing consistency across their applications. The Voluptuous library provides an easy-to-use API with few dependencies that supports basic Python data structures out of the box without requiring custom subclasses or complicated interfaces.
Voluptuous Python Library Example
Before we import, we must first install the Voluptuous Library if we haven’t already installed. Go to CMD (Command Prompt) or terminal and run the command given below:
pip install voluptuous 
The Voluptuous Library will now be imported. Next, we’ll import a new interface called “Schema.” And also import the validator you want to use, in my case it’s “ALL.”
from voluptuous import Schema
from voluptuous.validators import All
This is when Voluptuous enters the picture. It allows the user to specify the format in which data should be stored and retrieved.
The schema is a Python tree-like structure where nodes are pattern matched against related trees of values, according to the python manual.
Nodes can be values, types, or callable in this case. To put it another way, the schema is the data structure that we anticipate. An array of integers, a dictionary, or an array of dictionaries can be used.
schema = Schema({
'userId': All(int),
'title': All(str),
'completed': All(str),
})
We have a data structure or schema with three fields: UserId, which should be an integer value, title, which should be a string value, and finished, which should be a string value.
Dictionaries, lists, scalars, and validators make up schemas, which are nested data structures. Each node in the input schema is matched against corresponding nodes in the input data using a pattern matching algorithm.
schema = Schema(75)
print(schema(75))
schema = Schema('Codeleaks')
print(schema('Codeleaks'))
schema = Schema('BREAK THE CODE BARRIER. LET’S GET YOU SIMPLE SOLUTIONS FOR COMPLEX PROBLEMS.')
print(schema('BREAK THE CODE BARRIER. LET’S GET YOU SIMPLE SOLUTIONS FOR COMPLEX PROBLEMS.'))
In this way, we can use the Voluptuous Python Library for data validation. We can also use it with API, Schemas that getting exception and many more.
Complete Code
from voluptuous import Schema
from voluptuous.validators import All
schema = Schema({
'userId': All(int),
'title': All(str),
'completed': All(str),
})
schema = Schema(75)
print(schema(75))
schema = Schema('Codeleaks')
print(schema('Codeleaks'))
schema = Schema('BREAK THE CODE BARRIER. LET’S GET YOU SIMPLE SOLUTIONS FOR COMPLEX PROBLEMS.')
print(schema('BREAK THE CODE BARRIER. LET’S GET YOU SIMPLE SOLUTIONS FOR COMPLEX PROBLEMS.'))
Output
Conclusion
Validation is the process of checking data before you go ahead and process it. You should never trust your data, even if it’s coming from a source like Google Analytics! There are many reasons why validation can be necessary – to ensure accuracy, for example.
The Voluptuous Python library helps you check that all of your fields in JSON or XML files contain valid values without having to write code yourself.
It also automatically checks things like required fields, file size limits (i.e., only text files), UTF-8 encoding errors (which may cause parsing problems) and more on top of what standard libraries do.
