Creating A Database With Python – In this article I will walk you through everything you need to connect Python and SQL.
You’ll learn how to pull data from relational databases directly into your machine learning pipelines, store data from your Python application in your database, or any other use case you come across.
Creating A Database With Python
A quick note before we begin: There is a Jupyter notebook with all the code used in this tutorial available in this GitHub repository. Coding is highly recommended!
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The database and SQL code used here are from my previous Introduction to SQL series posted on Data Science (if you have any problems viewing the articles contact me and I can help you.
If you’re not familiar with the concepts behind SQL and relational databases, I’d point you in the direction of this series (and of course there’s a lot of great stuff!).
For data analysts and data scientists, Python has many advantages. The wide range of open source libraries makes it a very useful tool for any data analyst.
We have pandas, NumPy and Vaex for data analysis, Matplotlib, seaborn and Bokeh for visualization, and TensorFlow, scikit-lern and PyTorch (and many more) for machine learning applications.
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With its (relatively) easy learning curve and versatility, it’s no wonder Python is one of the fastest growing programming languages.
So if we’re using Python to analyze data, it’s worth asking – where does all this data come from?
Although there are many sources for datasets, in most cases – especially in enterprises – the data will be stored in a relational database. Relational databases are very efficient, powerful and widely used to create, read, update and delete any type of data.
The most widely used relational database management systems (RDBMSs)—Oracle, MySQL, Microsoft SQL Server, PostgreSQL, IBM DB2—all use Structured Query Language (SQL) to access and manipulate data.
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Note that each RDBMS uses a slightly different flavor of SQL, so SQL code written for one will generally not work in the other without (usually minor) modifications. But the concept, structure and operation are largely the same.
This means that for a working data analyst, a solid understanding of SQL is important. Knowing how to use Python and SQL together will give you an even greater advantage when it comes to working with your data.
I use Anaconda, but there are many ways to do this. Just google “how to install python” if you need more help. You can also use Binder to encode with the corresponding Jupyter notebook.
We will use MySQL Community Server because it is free and widely used in the industry. If you’re using Windows, this guide will help you set it up. There are also instructions for Mac and Linux users (although these may differ by Linux distribution).
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For this, we need to install the MySQL Connector Python library. To do this, follow the instructions, or just use the pipe:
We’re also going to use Pandas, so make sure you have that installed as well.
As with any project in Python, the first thing we want to do is import our libraries.
It’s a best practice to import all the libraries we use at the start of the project, so people who read or review our code know roughly what’s going on so there are no surprises. is
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At this point we have to configure the MySQL community server on our system. Now we need to write some code in Python that will allow us to connect to this server.
It is a best practice for code like this to create reusable functions, so that we can reuse it with minimal effort. Once it’s written you can reuse it in all your future projects, so in the future – you’ll thank yourself!
First line we name the function (create_server_connection) and name the arguments that this function will take (host_name, user_name and user_password).
The next line closes any existing connections so the server doesn’t get confused with too many open connections.
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We then use Python’s try-without block to handle any possible errors. The first part tries to establish a connection to the server using the mysql.connector.connect() method using the details specified by the user in the arguments. If it does, the function prints a happy little success message.
The no-block section prints the error that the MySQL server returns, in the unfortunate event that an error occurs.
We use it by passing the function’s output into a variable, which then becomes our connection object. Then we can implement other methods (such as cursors) and create other useful objects.
Now that we have established the connection, our next step is to create a new database on our server.
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In this tutorial we will only do this once, but then we will write it as a reusable function so that we have a nice useful function that we can reuse for future projects.
This function takes two arguments, the connection (our connection object) and the query (the SQL query we’ll write in the next step). It executes the query on the server through the connection.
We use the Cursor method on our Connection object to create a Cursor object (MySQL Connector uses an object-oriented programming paradigm, so there are many objects that inherit properties from parent objects).
This Cursor object has methods like execute, executeMany (which we will use in this tutorial) along with many other useful methods.
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If it helps, we can think of the Cursor object that gives us access to the Cursor cursor in the MySQL Server terminal window.
All of the SQL queries used in this tutorial are explained in my Introduction to SQL tutorial series, and the complete code can be found in the corresponding Jupyter notebook in this GitHub repository, so I won’t go into detail. SQL code does it? education
This can be a simple SQL query, however. If you can read English you can probably figure out what it does!
Executing the create_database function with the above arguments creates a database named ‘school’ on our server.
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Why is our database called ‘School’? Now is probably a good time to go into detail about what we are going to implement in this tutorial.
Following the example in my previous series, we’ll implement a database for an international language school—a fictional language training school that offers professional language lessons to corporate clients.
This Entity Relationship Diagram (ERD) organizes our entities (Teacher, Client, Course and Participants) and defines the relationships between them.
All information about what ERD is and what to consider when creating and designing a database can be found in this article.
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The raw SQL code, database requirements, and data to go into the database are all included in this GitHub repository, but you’ll see all of that as we go through this tutorial.
Now that we’ve created a database on the MySQL server, we can modify our create_server_connection function to connect directly to that database.
Note that it is possible – common, in fact – to have multiple databases on a MySQL server, so we always and automatically want to connect to the database we are interested in.
This is exactly the same function, but now we take one more argument – the database name – and pass it as an argument to the connect() method.
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The last function we will create (for now) is the most important one – the query execution function. This will take our SQL queries stored as strings in Python, and pass them to the cursor.execute() method for execution on the server.
This function is similar to our create_database function from earlier, except that it uses the connect.commit() method to ensure that the commands specified in our SQL query are executed.
This will be our workhorse function, which we will use (along with create_db_connection) to create tables, establish relationships between these tables, populate the tables with data, and update and delete records in our database.
If you’re a SQL expert, this function will allow you to run any and all complex commands and queries you have lying around, directly from a Python script. It can be a very powerful tool for managing your data.
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Now we are all ready to run SQL commands on our server and start creating our database. The first thing we want to do is create the necessary tables.
First we assign our SQL command (described in detail here) to an appropriately named variable.
In this case we use Python’s triple quote notation for multiline strings to store our SQL query, then we feed it to our execute_query function for execution.
Note that this multi-line formatting is for us code-reading humans only. Neither SQL nor Python ‘care’ if issued as an SQL command
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