SQL vs. Python: What's the Difference?

By Indeed Editorial Team

Published June 15, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data comes in many different formats, so data scientists, computer programmers, developers and software engineers benefit from knowing how to use common programming languages. Two such common programming languages are SQL and Python. If you're looking to start a career in computer science, it's important to learn the differences between these programming languages, their uses and their limitations. In this article, we compare SQL and Python, discuss when to use each one and describe which to learn first to start your computer science career.

Related: Is Computer Programming a Good Career? Definition and Tips

What is SQL?

SQL, which stands for Structured Query Language, is a programming language that allows developers to manage and retrieve information within a database or create their own databases. Many industries use relational databases—which use tables, columns and rows to organize information and link data between tables—to store information. SQL most often develops and maintains these databases.

Developers may also use SQL to produce quick data insights, perform data analyses and retrieve records from within extensive databases. Webpages, applications and enterprise software packages may all rely on the data stored in databases. Some examples of databases that SQL developers work with include:

  • Banking databases

  • Social media applications

  • Music software

Related: How To Become an SQL Developer

What is Python?

Python is a general-purpose coding language, which means that you can use it for a variety of programming tasks. Some of these tasks include back-end development, software development and writing system scripts. Data scientists often use Python because its simple syntax and popularity in the industry make it easy to collaborate with other data scientists when developing data analysis software.

Because of its ability to work with various platforms and its emphasis on readability, Python has become one of the preferred languages for data exploration. Many industries use software, applications and programs written in Python due to this versatility. Some uses for Python include general web development, data analysis and machine learning, which is a kind of artificial intelligence that focuses on developing computer algorithms that learn from experiences rather than manual updates to the coding.

Related: Differences Between R and Python: Which Should You Use?

SQL vs. Python

Here is some helpful information about SQL and Python to help you better understand their differences and uses:

Key differences

The key difference between SQL and Python is that developers use SQL to access and extract data from a database, whereas developers use Python to analyze and manipulate data by running regression tests, time series tests and other data processing computations. SQL's greatest advantage is its ability to combine data from multiple tables within a single database.

SQL is simpler and has a narrower range of functions compared to Python. Queries that SQL produces depend on functions, which are codes that perform specific tasks. However, SQL functions have fewer applications than Python. Instead of using functions, Python uses programming libraries, which can apply to a broad range of development projects. These programming libraries contain specific pieces and instructions for developing particular software or applications. For example, some Python libraries include:

  • Pandas for data analysis

  • PyPDF2 for manipulating PDFs

  • SciPy for numerical routines

  • NumPy for mathematical operations and scientific computing

  • Scikit-learn for machine learning

When to use SQL vs. Python

Python and SQL can perform some overlapping functions, but developers typically use SQL when working directly with databases and use Python for more general programming applications. Choosing which language to use depends on the query you need to complete. For example, a school district may maintain a database with information about all the schools within its jurisdiction. The database may contain entries for a dozen schools with independent entries that represent each school. Listed with each entry might be:

  • Demographic information about staff and students

  • Performance information about test scores and student grades

  • Budgetary information about funding for each school

A data scientist researching the funding that each different school in the district receives may need to query this dataset using either SQL or Python. Conducting the initial query to retrieve data in SQL may be a relatively simple procedure compared to conducting the retrieval in Python. However, subsequent computations, manipulations or analyses may be simpler to run using Python than with SQL.

Depending on the purpose of the query, the data scientist may choose to use the simpler SQL query to retrieve the information. However, if the data scientist plans to conduct additional analyses, they may use SQL for the initial query but use Python for the more complex computations. SQL has limited functions for processing, analyzing or experimenting with that data. Although SQL can run some data processes, it may be inefficient or complicated because the ability to perform calculations efficiently is not part of the language's design. Python is considerably more flexible and suitable for working with the extracted data.

Related: The 7 Best Programming Languages To Learn in 2021

Which language to learn first

Typically, SQL is a good programming language to learn first. As a tool, SQL is essential for retrieving content from relational databases. Compared to Python, SQL may be easier for some people to learn. SQL can also help you gain some basic knowledge of programming languages that may make it easier to learn other languages like Python. Because data retrieval is often the first step to any kind of high-level data manipulation, knowing how to use SQL may help you access the data you need before you can apply any Python queries to it.

However, knowing which language is right for you to learn first may depend on your goals and interests. Using both languages together may provide more benefits, but you do not have to know both languages to succeed in your computer science or data science careers. Learning Python can be an equally great way to start your career, especially if you plan to pursue a career as a Python developer. Many consider Python to be a beginner-friendly language because its syntax is comparable to English.

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