You can’t spell data science without data. Data is the most important part of data science. I can argue that all the field — and subfields — of data science is the science of interpreting what the data got to say. It’s the art of telling a story through your data.

Collecting, cleaning, and analyzing data are three of the main steps in any data science project. Most of the time — if not all — this data you use and need is stored in a DBMS (database management system) on a remote server or your hard drive.

So, you need to interact and communicate with this DBMS to store and retrieve data. To interact with the DBMS, you need to speak its language. That language is SQL (Structured Query Language). Over the years, people have referred to databases themselves as SQLs.

Recently, another term surfaced in the field of data science and databases, which is NoSQL databases. Whether you are just starting with data science or in the field for a while, you probably have heard of both SQL and NoSQL databases.

Whether to use SQL or NoSQL databases depends fully on your data and your target application. But, let’s say you already know which database schema you’re going to use…

The question now is — if you’re using Python…

Which Python library to use?

Well, this article is for you. In this article, I will cover the most known, used, and developed Python database libraries. We will talk about the library itself and what are the best causes to use each of them. We will start with SQL libraries first and then cover NoSQL ones.

Let’s get started.

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