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.