Python and SQL are two of the most popular programming languages. Although Python and SQL share some overlapping functionality, when it comes to working with data, they complement one another in a machine learning workflow. SQL is the most efficient way to query and transform data when working directly with databases while Python becomes an extension to prepared data in running more-complex data analytics, data wrangling, and machine learning tasks. In this article, we’ll explore these two languages, how they work together in data science applications, and the benefits of using Python to supplement SQL for data analysis.
Python and SQL
Python and SQL coding languages underpin many modern data processing, analysis, and machine learning applications. Together, they enable data scientists to fully realize the value of the data they gather from a multitude of sources.
SQL is a programming language used by developers to manage or retrieve data from within a database. As the standard language of relational database systems, developers use SQL to communicate with a database using SQL statements to execute tasks such as deleting data and updating data to or retrieving data from the database. SQL also has applications in data analysis and data science due to its simplicity in running large data transformations.
Python is a multipurpose coding language used in many computer programming applications. Python’s flexibility, simple syntax, and extensive libraries have made it popular with data scientists, and it is widely used in data analysis, data engineering, machine learning, and artificial intelligence applications.
How Does SQL Work with Python?
Before Python applications can interact with data in a SQL database or cloud data warehouse, a Python connector is required. The connector allows Python programs to access the database or cloud data warehouse. For example, connectors such as MySQL Connector allow Python programs access to MySQL databases. In Snowflake, the Snowflake Connector for Python serves a similar purpose, providing an interface for developing Python applications that can connect to Snowflake and perform all standard operations. In addition, Snowpark for Python lets users execute Python code inside Snowflake, with no need to move data or manage a separate environment.
Benefits of Using Python for Data Analysis
Using Python to interact with a cloud data warehouse for data analysis can help power more-robust, more-complex data analytics tasks. Here are six benefits:
One of Python’s main benefits is the availability of thousands of libraries. These powerful libraries can make quick work of data analytics tasks such as web scraping, data cleaning, visualization, and analysis. With an extensive collection of data analytics tools, Python is an ideal choice for complex data analytics tasks.
Robust community of users
Since Python is an open-source programming language, it comes with an extremely large number of enthusiastic, active users. This dedicated base of fellow programmers makes it easy to learn and grow, discovering new ways to put Python to use in data analytics applications.
Simple to learn and use
Another reason for Python’s popularity is it is one of the simplest, easiest-to-use languages. Its clear syntax and readability are designed to mimic human language, making it more intuitive and straightforward than many other coding languages.
Ease of creating data visualizations
There are multiple Python plotting libraries and other data visualization packages that make it an ideal tool for creating data visualizations. These libraries make it easy to present complex data in simple, easy-to-digest formats.
Coding in Python is clean and simple, requiring just a few lines of code. This makes it an ideal language for processing the massive amounts of data required in many data analytics use cases. Python code processes quickly, further reducing lag times in execution.
Snowflake for Data Science
The Snowflake Data Cloud accelerates your workflow with near-unlimited access to data and data processing power. With Snowflake, you can realize the full value of your models with a unified platform that enables cross-functional teams to build scalable data preparation, and model inference pipelines to transform data into actionable business insights.
Running Python in Snowflake is an effortless process using Snowpark for Python. Using this developer framework, Python developers can now enjoy the same ease of use, performance, and security benefits of the Snowflake elastic performance engine that also supports SQL, Java, and Scala. Using Snowpark’s rich set of functionality including the Snowpark API, Snowpark UDFs, and Stored Procedures, Python developers can build and deploy their Python code with ease. These features combined with the Anaconda integration provide the growing Python community of data scientists, data engineers, and developers with a variety of flexible programming contracts and effortless access to open-source Python packages to build secure and scalable data pipelines and machine learning workflows directly within Snowflake.
See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a Snowpark for Python free trial.