GraphQL Versus REST API

Ever since the introduction of GraphQL by Facebook, REST APIs have been pushed into the back seat, mostly due to the fact that the GraphQL API improves in the REST architecture by fixing many of its problems. However, GraphQL brings several problems of its own to the table, anyone who is familiar with coding will know that for every fix that a program provides, it bring an equal or greater number of problems as well. Since GraphQL is not perfect, it still has not entirely replaced the REST architecture, let’s take a look at the pros and cons of each architecture.

REST APIs are pretty much the standard architecture to go to for companies working on launching developer platforms, what made REST superior to protocols such as COBRA and SOAP is the fact that it does not require developers to have knowledge about libraries or special initialization. A great thing about REST is that it is designed to be HTTP friendly, this allows for easier programming and greater compatibility between different entities on the web. REST’s “tighter” architecture allows it to be simpler to use in contrast to other APIs, and at the same time it also has the ability to provide loose data contracts.

A bad thing about REST is its server driven selection which basically requires a developer to write a long and confusing line of code if they want to execute a specific command, GraphQL improves on this by making the required query smaller and cleaner. GraphQL also makes API’s less “chatty” by giving servers the ability to aggregate data into a one query, meaning that clients will not need to enter multiple fetch commands to access a certain piece of data.

GraphQL also has a more powerful tools than the REST API, allowing for more detailed query analytics. While the GraphQL makes APIs much easier to interact with, it also has several problems, such as the inability to follow HTTP’s caching specs, this means that developers need to oversee proper caching implementation by themselves. Another drawback of GraphQL is that that its arbitrary requests are exposed; a company will have no control over interactions between queries and third parties.

Also, GraphQL’s query structure is quite rigid and neither can it offer monitoring of HTTP specs like REST. Overall; GraphQL is new and therefore exciting, however it is not the perfect API that is going to replace the REST architecture. Both of these APIs have their own advantages and disadvantages that make them suitable for being used in different ways. We cannot say that the GraphQL is going to be taking over REST, however one can hope for improvements of the GraphQL in the near future as it continues to undergo further development.

Is Converting SQL Queries to Pandas Simple?

SQL has been one of the most handy data analysis languages out there for quite a long time, its simple declarative syntax makes forming queries quickly very easy. The kind of analysis that SQL is capable of carrying out is known as exploratory analysis; while it is straightforward and efficient, it is starting to become underpowered since nowadays we have a huge variety of data types. This is where the Python Data Analysis (Pandas) Library comes in; a new and improved data analysis method that is capable of doing everything that SQL can and a whole lot more.

Pandas is a library that is built specifically for data manipulation and analysis, it is great for use with structured data, has Anaconda support and is also open-source. The only problem with Pandas is that its syntax is so different that one needs to rewrite existing SQL queries if they want to use them with Pandas. Pandas works by applying operations on datasets and chaining them to let you reshape data however you want.

The rewriting process can be a bit tiresome at first, but once you get the hang of it you will realise that Pandas’ syntax is much simpler than SQL’s, an SQL command like “select*from shop” can be written simply as “shop” in Pandas, and connecting multiple conditions only requires you to place a “&” between them. Instead of having to write “select * from shop where category = ‘RTD ‘ and type = ‘Chocolate Milk’, you can simply write “shop[(shop.category  == ‘RTD’) & (Shop.type == ‘Chocolate Milk’)]

These are only a few examples of how one can rewrite their SQL queries for Pandas compatibility. Pandas’ simpler syntax can significantly reduce the amount of code that one has to write, this library has much more to offer as well; Pandas can be used to export data to a variety of formats, plot data that you have entered to create descriptive charts and share all of it on various platforms.

If SQL was your best friend then Pandas is about to become your bff, this simple and versatile syntax is a superb choice for anyone who wants to be able to handle a variety of data types and expand their data analytical capabilities.

Python: The Best Tool a Programmer Can Have

Python is one of those languages that can help you come up with a solution for almost any kind of problem, the best way to describe Python would be a hammer that can be used to deal with any kind of nail (as long as you know where to hit). You can find a lot of interesting uses of Python all across the internet, many of which are really fascinating and have incredibly simple coding.

If you are a beginner at Python then you should try your hand at the pyautogui library; it lets you control your mouse and keyboard through a Python program, this is just one of the many libraries that the Python language has to offer. You should keep in mind that while Python is incredibly versatile, it does take time to get the hang of it, after all, Python is a programming language.

Luckily, there is a ton of material related to Python that you can find online, some of which is free and some of which will need to be bought, once you have found material that is offering what you need, you can begin practicing away. When learning the basics of coding, it is important to study smartly; do not focus only on one skill, instead explore two or more at the same time, switching between them as you hit bumps and dead ends. This way you can cover more ground and also feel like you are making more progress.

Another thing that one should note is that you do not necessarily have to master a topic before making use of it, the best way to learn programming is to practically apply concepts as you pick them up. Practical implementation allows you to develop a greater understanding of how the concepts work and makes remembering them easier as well.

Remember; in coding, practice is what will make you perfect, so do not be afraid to experiment with a language as you learn it, not only will you develop a better understanding of the language, you will also have a great learning experience.