Data Validation is one of the most common steps in Data Processing. Although Python is a dynamically typed language that checks the data type a run time. This dynamically typed feature of Python makes it easier and more popular. As you always know great things come with high risk. Here the biggest risk is validating the data. In a statically typed language, It is easier to figure out invalid type data in the early stage. But in the python type of language, these issues are caught at later stages. But the Good news is – We have some stronger and developer-friendly Python Libraries. This article will explain to you – Top 5 Python data validation libraries.
Top 5 Data Validation Libraries in Python –
A big name in the data validation field of python. The colander is very useful in data validation from deserialized data. Basically crawled data from any web is deserialized. HTML, XML, and JSON have majorly opted data forms in validation. If you are also interested to validate your data ( HTML, XML, JSON ). Please have a look –
Here is the official link for colander documentation .
2. Cerberus –
The most developer is friendly in term of syntax. Let me make this explanation (why Cerberus ?) more interesting for you. If you recall the best mobile app which you find most easy to operate. If you closely look that you will find why it is so easy to operate. Actually, most of the time when you get something with which you are more familiar. That matches your mind pattern. This causes no stress on the mind while using and we most enjoy that. After all, we do not want any stress at all. The same happens when you are a developer and exploring any new Library. If you find some similar type of API that you have already explored earlier. You must get a smooth learning curve on that.
3. Schematics –
Well if you remember, At the very beginning of the article. I have mentioned the dynamic type nature of python language and related issues with that. This library can address most issues. Basically, it helps to validate the python data structure. schematics is also having good documentation. Here is the official link for Schematics documentation.
4. Schema –
Quite similar to the above one. This also helps to validate the python data structure. Basically when you read some data from external sources like config files etc. You are assuming that it will fit into your coded data structure. While unit testing we also put them in the correct way. But we can not enforce anybody to provide in the correct ways. We can only make/build our virtual data guards which will stop invalid data flow in our system. These Libraries play an important role in this.
JSON is the most popular data transfer format between systems. This python validation library helps to validate JSON data from various angles in python. What I love about Jsonschema is – The way it handles the validation error. By using this python schema validation library, You may draw a validation error tree on the top of this library. I will suggest you have a quick view of Jsonschema.
6. Voluptuous –
This Python data validation library is widely used in the REST API data exchange. especially JSON and YML data format validation.
It is quite a customizable and adaptive python input validation library. You may perform the validation by creating a custom adapter as well. Here is the complete documentation for the Valideer Python module.
Why Data Validation Libraries are essentials-
So far we have seen what are Data Validation Libraries. Now let’s explore why are really required them. Can we not write those rules in core python? The answer is pretty simple – Yes you can. See the thing is you have to waste a lot of time writing your own custom rules in the place that using these APIs/Libraries can save tons of time for you. One thing you should only keep in the mind is license behind the library.
Yes ! the most important thing behind using any open source is the license and terms of distribution. As I have seen so many Libraries and frameworks that are free but when you are integrating with some profit-making products they are chargeable. So make sure when you choose any third-party library.
So, friend, I hope this article must solve your problem in finding the Right python validation framework. If you have something which you can contribute to data validation libraries of python. Data Science Learner Team will appreciate your comments and emails. Basically, we promote collaborative learning. This collaborative learning is only possible when the reader interacts and reverts back. Anyways let me tell you one more important thing. The above ranking does not mean the second-place data validation libraries are not as good as the first one. This is basically an order to document them at a place. All of the above mentions are equally good. It completely depends on the data you have and the use case of that as well.
Data Science Learner Team
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