Are The New M1 Macbooks Any Good for Data Science? Let’s Find Out (2024)

The new Intel-free Macbooks have been around for some time now. Naturally, I couldn’t resist and decided to buy one. What follows is a comparison between the 2019 Intel-based MBP and the new one in programming and data science tasks.

If I had to describe the new M1 chip in a single word, I would be this one — amazing. Continue reading for a more detailed description.

Data science aside, this thing is revolutionary. It runs several times faster than my 2019 MBP while remaining completely silent. I’ve run multiple CPU exhaustive tasks, and the fans haven’t kicked in even once. And, of course, the battery life. It’s incredible — 14 hours of medium to heavy use without a problem.

But let’s focus on the benchmarks. There are five in total:

• CPU and GPU benchmark
• Performance test — Pure Python
• Performance test — Numpy
• Performance test — Pandas
• Performance test — Scikit-Learn

If you’re reading this article, I’m assuming you’re considering if the new Macbooks are worth it for data science. They aren’t “deep learning workstations” for sure, but they don’t cost that much, to begin with.

All comparisons throughout the article are made between two Macbook Pros:

• 2019 Macbook Pro (i5–8257U @ 1.40 GHz/8 GB LPDDR3/Iris Plus 645 1536 MB) — referred to as Intel MBP 13-inch 2019
• 2020 M1 Macbook Pro (M1 @ 3.19 GHz/8GB) — referred to as M1 MBP 13-inch 2020

Not all libraries are compatible yet on the new M1 chip. I had no problem configuring Numpy and TensorFlow, but Pandas and Scikit-Learn can’t run natively yet — at least I haven’t found working versions.

The only working solution was to install these two through Anaconda. It still runs through a Rosseta 2 emulator, so it’s a bit slower than native.

The test you’ll see aren’t “scientific” in any way, shape or form. They only…