Is Python Still Worth Learning for Data Analysts in 2026?

Someone messaged me last week. "I am learning Excel and SQL right now. Should I stop and switch to Python instead? Everyone online says Python is the future." I hear a version of this question almost every month. Let us look at what the data actually says, not what a YouTube thumbnail says.

Think of it like learning to cook

Imagine you want to become a chef. You would not start by buying an industrial kitchen with ten machines you do not know how to use. You would first learn to chop, season, and control heat with basic tools. Once you can cook well with a knife and a pan, a food processor makes you faster. It does not replace the skill, it multiplies it.

Python works the same way for a data analyst. It is the food processor, not the knife. It speeds up work you already know how to do by hand. It is not the first thing you pick up.

What is actually happening

Python is a programming language. For data analysts, it is mostly used to clean messy data, automate repetitive reports, and do analysis that a spreadsheet cannot handle well, like working with very large files or building simple models. It is genuinely useful. The question is not whether Python is good. It is whether you need it on day one.

Here is what the numbers show. SQL still shows up in over 80 percent of data analyst job postings, making it the single most requested skill in the field. Python's presence varies a lot depending on the source, showing up in roughly 30 to 50 percent of data analyst postings. That is a real jump from a few years ago, but it also means it is still not universal, and for true entry-level roles, it is rarely a hard requirement.

On pay, the picture is more interesting. Analysts who combine SQL, a BI tool like Power BI or Tableau, and Python tend to earn more than analysts who only know Excel and SQL. So Python does add to your paycheck, but it adds on top of a foundation, not instead of one.

The honest nuance

Most experienced analysts and hiring managers recommend a specific order: Excel first, because it teaches you how to think about rows, columns, and filters. Then SQL, because that is how you actually pull data out of a company's real systems, and it is the skill that shows up as non-negotiable in job listings. Python comes after, when you hit a wall that spreadsheets and SQL cannot solve on their own, like automating a report you build every week or handling a dataset too large for Excel to open smoothly.

Skipping straight to Python without SQL is a common mistake I see from self-taught beginners. It usually backfires in interviews, because the take-home tests and screening questions are built around SQL and spreadsheet logic, not Python syntax.

What this means for you as a learner

If you already have SQL and Excel down, learning Python next is a smart move and will likely help your salary and your range of job options. If you are still early and have not touched SQL yet, do not switch tracks because of a trending video. Finish building your SQL and spreadsheet foundation first. Python will still be there, and you will learn it faster once you already understand how data logic works.

Where to go from here

Python is worth learning. It is just not the first rung on the ladder, it is the third or fourth. If you want a clear sense of what to learn next based on where you actually are, our free AI Readiness Assessment gives you a starting point without any sales pitch attached, and our Labs let you practice SQL and Python hands-on at your own pace.