Dario Radečić

Spoiler: It’s harder than you think. Dario Radečić · Follow Published in Towards Data Science · 8 min read · 21 hours ago — Article thumbnail (image by author) Colors are hard, especially if you don’t have an eye for design. Most of us tech professionals don’t. The upside is that Python can do most of the heavy lifting for you. It can generate an entire monochromatic color palette that’ll look stunning on any stacked

The best part? You’ll only have to do this once. Dario Radečić · Follow Published in Towards Data Science · 7 min read · 8 hours ago — Article thumbnail (image by author) Every Matplotlib chart has the potential to go viral. But not with the default theme. Let’s be honest: default styles aren’t going to stop anyone in their tracks. If you want to make your reader pay attention, you’ll need more than a

Matplotlib charts are an eyesore by default — here’s what to do about it. Dario Radečić · Follow Published in Towards Data Science · 5 min read · 8 hours ago — Article thumbnail (image by author) Data visualization offers much deeper insights than looking at raw, numerical data. However, creating appealing charts takes time and effort. Matplotlib is a de facto standard library for data visualization in Python. It’s simple, has been used for

Which features carry the most weight? How do original features contribute to principal components? These 5 visualization types have the answer. Dario Radečić · Follow Published in Towards Data Science · 8 min read · 11 hours ago — Photo by Andrew Neel on Unsplash Principal Component Analysis (PCA) can tell you a lot about your data. In short, it’s a dimensionality reduction technique used to bring high-dimensional datasets into a space that can be

Poetry makes deploying machine learning applications a breeze — learn how! Dario Radečić · Follow Published in Towards Data Science · 8 min read · 12 hours ago — Photo by Prachi Gautam on Unsplash If I had a dollar every time I faced a missing Python dependency or a version mismatch, well, I wouldn’t be a millionaire, but you get the point. Dependency management is a common problem in data science with many potential

Moving data around can be slow. Here’s how you can squeeze every bit of performance optimization out of Python. Dario Radečić · Follow Published in Towards Data Science · 9 min read · 13 hours ago — Photo by Matthew Brodeur on Unsplash Python is often criticized for being among the slowest programming languages. While that claim does hold some weight, it’s vital to point out that Python is often the first programming language newcomers

The one billion row challenge is exploding in popularity. How well does Python stack up? Dario Radečić · Follow Published in Towards Data Science · 10 min read · 9 hours ago — Photo by Alina Grubnyak on Unsplash The question of how fast a programming language can go through and aggregate 1 billion rows of data has been gaining traction lately. Python, not being the most performant language out there, naturally doesn’t…

Process huge volumes of data with Python and DuckDB — An AWS S3 example. Dario Radečić · Follow Published in Towards Data Science · 4 min read · 7 hours ago — Photo by Growtika on Unsplash When companies need a secure, performant, and scalable storage solution, they tend to gravitate toward the cloud. One of the most popular platforms in the game is AWS S3 — and for a good reason — it’s an

SQL can now replace Python for most supervised ML tasks. Should you make the switch? Dario Radečić · Follow Published in Towards Data Science · 8 min read · 10 hours ago — Photo by Resource Database on Unsplash When it comes to machine learning, I’m an avid fan of attacking data where it lives. 90%+ of the time, that’s going to be a relational database, assuming we’re talking about supervised machine learning. Python is

Looking to speed up your data processing pipelines up to 10 times? Maybe it’s time to say goodbye to Pandas. Dario Radečić · Follow Published in Towards Data Science · 7 min read · 12 hours ago — Photo by Hans-Jurgen Mager on Unsplash In a world where compute time is billed by the second, it’s only logical to minimize it as much as you can. And then some. Python’s vast data processing ecosystem is