When working with SQLAlchemy, the starting point is the Engine. We won't need to write any SQL to do this or worry about database specifics. Objects can be stored (persisted) to and loaded from the database.Īs you'll see later, we'll create Python Objects from our CSV data and use SQLAlchemy to store them. ORMs allow us to map Objects in our code to tables and columns in a database. To bridge across these two different systems, we use an ORM or Object Relational Mapper. However, in a relational database, data exists in tables with columns and rows. Languages like Python are Object-Oriented, a programming paradigm where code is organized into 'Objects' that have data (attributes) and methods (functions or behavior). You can find the CSV file and final code in the accompanying repository for this tutorial on my GitHub.īefore using SQLAlchemy, we need to cover off a related concept: ORMs. I've chosen this dataset because its typical of the kinds of datasets encountered in the real world. Familiarity with SQL and working with databases.įor this tutorial, we'll be using the New York City Airbnb Open Data from Kaggle's excellent public dataset repository.Įach row of the CSV represents a listing on Airbnb and includes a range of data types.Familiarity with Python, installing packages, importing them into your code, and running a Python script. ![]() In writing this, I'm assuming a couple of things: The article is not intended to be a definitive tutorial on SQLAlchemy but aims to show how you can take advantage of its features to move data into a database. ![]() SQLAlchemy is an excellent choice of ETL tool because it abstracts away a lot of the database-specific code and lets you work just in Python. In this article, we'll look at another method using SQLAlchemy, a popular Python library for working with databases. In the first article of this series, we looked at how to move data to a Database with just Python. Python is an excellent choice for this kind of task. However, external data isn't always straightforward, and sometimes you need a tool with flexibility and granular control over the process. When analyzing data, a common task is moving data from a source to a database.
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