Here’s the uncomfortable fact about Python within the enterprise: The language is straightforward; the ecosystem just isn’t. Most builders can write readable Python by week two. What derails them—and subsequently your schedules—is the whole lot across the language: the undertaking scaffolding, packaging, imports, testing, and the information stack the place Python earns its maintain. All these points had been laid naked within the replies to Python professional Matt Harrison’s query, “What is your biggest struggle with learning Python?” The replies didn’t complain about syntax; they had been about the whole lot orbiting it. If you lead a crew of builders, that’s your cue to spend much less time targeted on for loops and extra time on paving a dependable street by way of Python’s vibrant, sophisticated ecosystem.
If you’re questioning whether or not the battle is price it, the market has already answered. Python surged once more within the 2025 Stack Overflow survey—up seven proportion factors yr over yr—pushed by AI and knowledge workloads. For builders and the technical leaders who allow them, investing in Python proficiency isn’t non-obligatory; it’s desk stakes for contemporary engineering.
I’ve argued for years that Python turned the lingua franca of AI not as a result of it’s the quickest language however as a result of it’s the shortest distance from concept to working code. But that doesn’t imply it’s simple. If you’re a supervisor, your job is to take away the friction that forestalls Python from compounding into enterprise worth.
Paving the event path
Harrison’s thread surfaces the identical themes I usually hear about Python from builders in giant firms: setting setup, packaging and dependency drift, complicated imports, shaky psychological fashions for dataframes, and a hazy line between “fast enough” prototypes and production-ready providers. These will not be insurmountable points. All of them are amplified by organizational indecision—too some ways to begin a undertaking, too many “standard” instruments, too few high-signal examples.
In different phrases, your groups aren’t failing at Python; they’re failing at decisions.
When leaders ignore this, Python appears to be like fickle. Builds go on a laptop computer and fail in CI (steady integration). Two groups select two packaging techniques and might’t share a library. Data scientists write right code with painful efficiency as a result of nobody taught vectorization as a primary precept. Developers mindlessly embrace async with out understanding when concurrency helps. Each…
