My team has been advised to stop using Jupyter notebooks for our data science projects. Our manager believes that notebooks can lead to poor coding practices and complicate the process of converting our exploratory work into functional code.
I am accustomed to conducting my exploratory data analysis, prototyping, and visualizations in notebooks, and then I would refine that code for APIs or final outputs.
Now, I need to explore alternative methods. What tools and workflows do you recommend for data exploration, chart creation, and model building without relying on notebooks? How do you manage the transition from initial analysis to production-quality code?
I would appreciate any advice to help my team make this transition smoothly.
totally agree! switching to scripts is a good move. also, using version control like git helps maintain your workflow. plus, don’t skimp on comments in the code—it’ll make your team’s life easier when revisiting projects.
I made this exact switch about two years ago when our team ditched notebooks. The trick is setting up a solid dev environment with IDE features that give you back some of that interactive feel. VS Code’s Python interactive window or PyCharm’s scientific mode really helped during the transition. For exploring data, I started writing modular functions in separate files and leaned heavily on the debugger to check variables and intermediate results. My workflow got more structured - separate scripts for loading data, preprocessing, analysis, and viz, then importing modules as needed. Felt slower at first, but code quality improved within weeks. Bonus: everything became way more reusable across projects since it was already in proper function format.
Your manager’s right - notebooks create messy habits. Dealt with this exact issue three years ago.
Built a proper data pipeline from scratch. Ditched notebooks for plain Python files with main functions I could run piece by piece. Multiple small scripts - data loading, cleaning, different analyses.
Miss the interactive stuff? Use IPython in terminal. Import modules, test functions instantly. Cleaner than cells but you get the same quick feedback.
Visualization got easier when I wrapped plotting into functions. Now I just call plot_distribution(data, ‘column_name’) instead of copying matplotlib everywhere.
Game changer was treating everything as packages from day one. Analysis becomes importable modules that production can use directly. No rewrites - just import and run.
Two weeks to get comfortable, but code reviews become way cleaner.
Your manager’s right - notebooks are technical debt nightmares. Been there, and there’s a much better way than manual scripts and IDE hacks.
Just automate your whole pipeline from day one. Skip the separate scripts for each step. Build workflows that handle data ingestion, cleaning, analysis, training, and deployment automatically.
I use Latenode for this. It connects data sources, runs Python scripts automatically, and triggers visualizations or model updates on schedules or when data changes. No more manual script running or terminal imports.
Game changer: exploratory work becomes automated experiments. Instead of running cells to check results, I set up workflows that test different parameters and log everything automatically. Data viz gets generated and sent to stakeholders without me lifting a finger.
When you need production deployment, you’re already there. No rewrites because automation forces good practices upfront.
My team went from 60% manual execution to pure analysis focus. Takes maybe two weeks to transition, but you’ll never go back.
Had the same transition forced on me last year. Test-driven approach saved me - write small functions that do one thing, then quick tests to verify they work. This replaces the cell-by-cell checking you’d do in notebooks. For data exploration, I create a config file for each project. Way better than hardcoding paths and parameters everywhere. Also switched to logging instead of print statements. Sounds like overkill but debugging becomes so much easier when things break. The real win was collaboration. Code became readable and maintainable in ways notebooks never were. Takes about a month to feel natural, but it’ll make you a better programmer.