In this article, I am going to demonstrate how to connect to databases using a pandas dataframe object. Pandas in Python uses a module known as SQLAlchemy to connect to various databases and perform database operations. In the previous article in this series “Learn Pandas in Python”, I have explained how to get up and running with the dataframe object in pandas. Using the dataframe object, you can easily start working with your structured datasets in a similar way that of relational tables. I would suggest you have a look at that article in case you are new to pandas and want to learn more about the dataframe object.

A brief about SQLAlchemy

To talk about the SQLAlchemy in brief, it can be referred to as an ORM (Object Relationship Mapping), too, which is written in Python to work with databases. It helps programmers and application developers have full control flexibility over the SQL tools. Often, while developing applications in any programming language, we come across the need to store and read data from the databases. This module provides a pythonic way to create and represent relational databases from within the Python projects. An advantage of working with such a module is that you do not need to remember the syntactical differences of the various databases around. The module does all the heavy lifting for you, while you interact with all the databases in the same way.

You can read more about the module from the official website.

Installing the module in Python

Also, for the purpose of this tutorial, I am going to create a virtual environment and do all the necessary demo from within the environment. You can run the following command to install it in your environment in Python.

#python #sqlalchemy #pandas dataframe