Python vs. SQL for Data Analysis

You surely have heard about SQL and Python. Maybe you’ve even worked with one of those languages. Both have strengths and weaknesses. When it comes to data analysis, which should you use? This article will demonstrate how Python and SQL are useful for data analysis and how knowing both languages can boost your data analysis journey.

Decided to get into data analytics? Great! An increasing number of companies are looking for people who can analyze data and draw conclusions from it. But there’s another challenge: what tools will you use for this? Don't worry, I'll help you with that. The most obvious choices would be SQL or Python.

If you decide to learn SQL for data analysis, start with the SQL Basics course. It's interactive, 100% online, and will teach you everything you need to know to get started. This is the best choice if you are serious about your career. Prefer to start with Python? The Python Basics track on our sister site LearnPython.com is a good choice. And the first course in that learning track is completely free!

But wait, why am I offering you these particular languages and what exactly are they? Read on to find out.

A Brief Introduction to SQL and Python

SQL

SQL, an acronym for Structured Query Language, is a programming language used for working with relational databases. With SQL, you can extract, modify and delete information from a database. You also can modify the structure of the database itself.

SQL is astonishingly friendly for beginners: you can perform complex operations with very short, straightforward, and understandable requests. Do you want a clear guide on how to learn SQL effectively and painlessly? Here is The Best Way to Learn SQL: A Comprehensive Guide for Beginners.

Let’s figure out the following example. Imagine that you sell sports equipment online and all your product information is located in a table called products in your database. You want to retrieve the price of one of your products whose SKU is A5E4EQZWE; you can do that with the following request:

SELECT price FROM products WHERE sku=’A5E4EQZWE’;

Simple, right? Now, you can change this product’s price in your database by executing the following request:

UPDATE products SET price=25.5 WHERE sku=’A5E4EQZWE’;

If you want to discover some badass SQL requests, read the article Top 7 Advanced SQL Queries for Data Analysis by Nicole Darnley.

SQL is also extremely efficient. It can handle super-heavy and complex requests in a short time, making it the perfect ally for data analysis! SQL is used even by non-technical people: sales, marketing, and finance teams (among others) use SQL to extract, process, and analyze information and to make decisions based on data.

SQL is great, but it has some limitations. First, it is important to mention that SQL is a standardized language; there are many small variations of SQL called SQL dialects. MySQL, PostgreSQL, and Microsoft SQL Server, for example, are SQL dialects. But don’t worry; SQL dialects are mutually intelligible, and switching from one to another is not a big deal. Interested in databases? You should read the excellent article The Most Popular Databases in 2023 by Kamila Ostrowska.

SQL is a domain-specific programming language, meaning that it is used for a specific thing: working with databases. You cannot build an application or create complex algorithms with SQL. But if you’re using SQL for a purpose like data analysis, that limitation really doesn’t matter.

SQL is a game-changer. Even if you're new to coding, it's easy to pick up and start diving into big sets of data. With just a few commands, you can pull out interesting facts and figures from a sea of information. If you’re wondering if SQL is too challenging to master, check out Jill Thornhill’s article Is SQL Hard to Learn?.

What's more, SQL works everywhere. Whether you're using a small computer database or a big online system, SQL is the go-to tool. As technology keeps changing, SQL stays relevant, making it a trusted tool for anyone working with data. In short, SQL is both user-friendly and powerful, a rare combination in the tech world.

Python

Unlike SQL, Python is a general-purpose programming language: you can do almost everything with Python! You can build a website, create a desktop application, write complex algorithms, or run scripts. You can even create games with Python!

According to the StackOverflow Developer Survey 2023, Python is one of the most popular programming languages for people learning to code. Beyond the trendiness, Python is an excellent programming language for beginners: it has an easy-to-understand and easy-to-write syntax. The following Python code calculates the circumference of a circle with a given radius:

import math

def calculate_circumference(radius):
    return 2 * math.pi * radius

radius = 2
circumference = calculate_circumference(radius)
print(f"The circumference of the circle with radius {radius} is {circumference:.2f}")

Quite simple and understandable, right? The syntax is clean; the code is not flooded by redundant brackets or parentheses. In Python, indentation is mandatory; this reduces spaghetti code. Python’s imperative programming style allows you to produce clean code and increase your productivity. It is also very satisfying to read another programmer’s clean code. You don’t waste much time figuring out the code, which means you can focus on the main matter (e.g. implementing a new feature).

Python is multi-purpose, but it’s also the key language for data science. There are a multitude of awesome Python libraries and frameworks for data analysis and machine learning! The most famous Python library for large dataset manipulation is undoubtedly NumPy. NumPy is the result of huge collaborative work by the Python community; it’s an essential tool for data scientists.

Python libraries are fascinating; if you want to know more about them, please read Python Libraries You Need to Know in 2023 by Soner Yildirim.

Knowing Python can mean a bigger paycheck. Many companies value Python skills and are ready to pay well for them. So, if you're looking to increase your salary, learning Python is a smart move.

There's a huge demand for Python experts. From creating websites to building smart tech, Python is used everywhere. Big companies and new startups are always searching for people who know Python development. And with its growing community and updates, Python's popularity isn't slowing down. So, learning Python means you're setting yourself up for lots of job opportunities now andn the future.

Additionally, widely used web scraping with Python, enables you to automate data extraction from websites, a skill highly valued in many industries.

And the best part is that Python is easy to learn. It's straightforward and reads almost like regular English. This makes it great for beginners. But it's not just for newbies; even experts love it because it's powerful enough to handle big tasks. In short, it's both simple and strong, a perfect combo.

Python vs. SQL for Data Analysis

In this section, I will dive into specific areas of SQL for data analysis, comparing its capabilities with Python to determine which is a better fit.

First of all, let’s speak about a crucial step in the data analysis process: data cleaning. Before analyzing data, it’s important to ensure that the data is accurate and reliable. It’s necessary to identify and correct errors, inconsistencies, and inaccuracies in a dataset.

Generally, SQL is preferred for data cleaning: most of the cleaning operations are straightforward (for example, removing rows with missing data) and can be performed with simple SQL requests. Also, SQL handles large datasets easier than Python and often leads to a better execution time. However, if you need to perform complex operations for cleaning your data, SQL can become tricky. Using a Python library like NumPy or pandas can be a better fit.

Next, we have data manipulation. After extracting and cleaning your data, you will probably need to arrange it to make it easier to understand and interpret. SQL can be used for basic operations, but Python is generally preferred for data manipulation: libraries like NumPy or pandas contain most of the functions you need.

Once you have cleaned and manipulated your data, you can visualize it! For basic data visualizations (e.g. sales over time), you can use data analytics tools like Metabase, which is based on SQL requests. It produces astonishing interactive charts (pie, waterfall, etc. ) that you can show to the stakeholders or include in a report. If you need more advanced visualizations, you can use the Python libraries Matplotlib and seaborn, which provide a wide range of 2D and 3D plotting functions.

Finally, there’s machine learning! Once your data is clean and well-organized, you can use it to create predictive models. No SQL here; you can achieve this with Python and two of its popular libraries for machine learning: scikit-learn and TensorFlow. Both libraries provide algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for data preprocessing, model selection, and evaluation.

As you can see, using SQL for data analysis is as crucial as Python; both have their unique strengths. I strongly recommend you learn both these programming languages. Need help plotting your course? Kateryna Koidan wrote a Roadmap to Becoming a Data Analyst; you should read it!

SQL vs. Python: Which Is Better for Data Analysis?

When it comes to choosing between SQL and Python for data analysis, it's like picking between apples and oranges. Both are fantastic in their own right. SQL is a powerhouse for managing and querying large datasets directly from databases. Its precision in extracting specific data points is unmatched, making it a favorite for many data analysts.

On the other hand, Python’s versatility shines. It's not just about data analysis; with Python, you can venture into web development, machine learning, and so much more. Its libraries make data manipulation and analysis a breeze. For those looking to dive deep into data and pull out insights, Python is a trusted companion.

But here's the thing: there's no need to pick one over the other. In the world of data analysis, using SQL and Python together can be awesome. Imagine using SQL to fetch data and then employing Python's tools to analyze and visualize it. It's like having the best of both worlds.

So, who wins in the Python vs. SQL for data analysis debate? The answer is simple: Both are champions in the realm of data analysis. If you're aiming to be a top-notch data analyst, mastering both SQL and Python is the way to go. They complement each other, ensuring you're well-equipped to tackle any data challenge that comes your way.

As I’m writing these lines, the All Forever SQL Package – which includes all LearnSQL.com’s interactive courses – is available with a humongous discount! Check it out!

Thanks for reading this article; I really hope you liked it!