I'm a systems engineer who moved into analysis, with years of experience using technical tools such as RStudio, MATLAB, and QGIS, plus some HTML, JavaScript, and VBA. My current employer wants me to use Python to develop algorithms that can eventually be integrated into products by the software engineering team.
I've been trying to learn with Spyder and PyCharm by following online examples, but I regularly run into missing dependencies, incompatible library versions, outdated instructions, or other errors that prevent the examples from working. It feels as though Python code only works in the exact environment where it was originally written. Is this a normal part of working with Python, and what is the best way to create projects that can be reliably handed over to other developers?
4 Answers
A lot of online examples leave out important setup details or were written for an older Python version. Check the project’s README, confirm which Python version it expects, and look for its dependency file before running the code. If a package is abandoned or incompatible with modern versions, it may be more efficient to choose a maintained alternative rather than spending days repairing it.
If the code needs to run on other machines, an isolated and reproducible environment is important. A virtual environment is usually enough for ordinary projects; a container can be useful when the operating system and system libraries also need to be identical. The goal is not to bundle every Python package ever made, but to record and isolate only the dependencies that the project actually uses.
Dependencies are a normal part of Python development, but they should be managed per project rather than installed randomly into your main Python installation. Start with a virtual environment, then install the project’s dependencies from a requirements.txt or pyproject.toml file. Tools such as pip, uv, Poetry, or Conda can help with this. A properly configured project should document its Python version and required packages so another developer can recreate the same environment.
For your situation, ask the software engineers at work which tools and package versions they already support. Since you’re prototyping algorithms that will be integrated into a larger product, matching their preferred Python version, project layout, testing approach, and dependency management will make the handoff much easier. You may only need a small set of well-supported libraries rather than exploring the entire Python ecosystem.
Modern tools such as uv can create the environment, install dependencies, and lock exact versions with much less manual work. That can be a good starting point if traditional virtual-environment instructions feel confusing.