Don’t Ever Say Python Sucks

We’ve all heard it before. Maybe you’re at a tech meetup, coding competition, or just mindlessly scrolling through Twitter—someone inevitably chimes in: “Python sucks. It’s too slow for real applications.”

Cue the eye-roll.

I get it, really. Python has its limitations. It’s dynamically typed, which makes it slower than statically-typed languages like C++ or Go. But dismissing Python outright as some kind of toy language for hobbyists? That’s missing the point entirely. In fact, it’s downright ignorant.

Welcome to the Python Paradox

Python’s been called everything from the “glue language of the Internet” to “the language that’s too slow to matter.” It’s been at the forefront of some of the biggest tech movements in recent history: machine learning, web development, automation, and even DevOps. Despite this, it’s constantly put under the microscope by performance purists who seem to have a religious devotion to benchmarks. Here’s a pro-tip: just because your program can run faster doesn’t mean it’s any better.

The funny thing? Many of these critics end up quietly using Python in the background when they need to get things done. It’s like a chef who scoffs at pre-packaged ingredients but still keeps a few boxes of pasta in their pantry—because sometimes, convenience and simplicity trump performance and elitism.

The Illusion of Performance

For all the noise about Python’s performance bottlenecks, have you ever stopped to think whether most applications need to be micro-optimized? Sure, if you’re working on a high-frequency trading platform or squeezing out cycles in a GPU-heavy environment, by all means, knock yourself out with C++, Rust, or whatever language lets you measure nanoseconds. But 99% of the time, your business application’s bottleneck isn’t whether a loop executes in 0.01 or 0.1 seconds—it’s that nobody’s using your application in the first place.

And that’s where Python shines. It lowers the barrier to entry for complex domains, enabling more people to build, prototype, and ship ideas. If you can build a robust machine learning model in a fraction of the time it would take in some faster, low-level language, that’s worth its weight in gold.

The Real Productivity Boost

I’ve seen developers burn through entire weeks optimizing code to squeeze out that last 5% of performance, only to end up in a quagmire of convoluted, unreadable spaghetti that even they can’t understand a month later. Python developers, meanwhile, have often moved on to three new projects in that time—prototyping, iterating, and getting real-world feedback.

That’s the dirty secret behind Python’s “slowness”: It’s fast where it counts—in human time, not machine time. So the next time someone tells you “Python is too slow for real-world use,” just remind them of the hours they’ve wasted debugging their handcrafted bitwise operations. I’ll bet the time saved by Python’s straightforward syntax and huge ecosystem more than makes up for the milliseconds lost in execution speed.

But hey, if performance is such a big deal, here’s a simple trick for optimizing your Python code: Don’t write your bottlenecks in Python!

A Performance-Oriented Mindset

Python has tools like Cython, PyPy, or native bindings to let you offload those high-performance sections to faster languages. Want a numerical computation to run faster? Delegate it to a C-based library like NumPy. Need concurrency? Use async/await or throw in a bit of Rust and call it from Python. This language isn’t trying to beat C++ at its own game; it’s a versatile Swiss Army knife that knows how to play well with others.

But that nuance often escapes the armchair critics. They’d rather wax poetic about how they compiled their entire codebase to assembly to shave off a few microseconds on their pet project. Good for them. Meanwhile, Python developers are shipping solutions to actual problems that people care about.

Let’s Talk Ecosystem

Want to build a web app? Python’s got Django and Flask. Need to analyze data? Pandas and SciPy have you covered. Looking to automate DevOps workflows? Ansible and Fabric are at your fingertips. How about some AI wizardry? TensorFlow and PyTorch were practically born in Python. If you look at Python and only see a “slow” language, you’re ignoring the most comprehensive and battle-tested ecosystem out there.

The truth is, Python isn’t meant to be the fastest language on the block. It’s designed to be a high-level language that’s easy to read, write, and maintain. It’s the language that minimizes cognitive overhead so you can focus on solving the problem at hand, not wrestling with syntax or boilerplate code.

“Python Sucks” — A Litmus Test

Whenever someone says, “Python sucks,” what they’re really saying is: “I don’t know how to pick the right tool for the job.” Because if you did, you’d recognize Python’s strengths and limitations—and know when to use it and when to move on. Sure, it’s not the best for real-time systems, embedded devices, or ultra-low-latency applications, but for everything else, it’s more than sufficient.

In reality, it’s the person who blames the tool instead of their own limitations that’s the real issue. Those who understand Python’s value don’t complain—they adapt. They use it for what it’s good at and seamlessly integrate it with other languages when necessary.

Conclusion: It’s About the Journey, Not the Runtime

At the end of the day, coding is about solving problems, not about how fast you can execute an arbitrary loop. Python accelerates development time, supports quick experimentation, and has one of the most supportive communities around.

So, the next time someone dismisses Python for being “slow,” just nod and let them chase their performance metrics. Meanwhile, you’ll be over here, sipping coffee and actually building things that matter.

Because in the real world, shipping useful code always beats winning benchmarks nobody cares about.