I’m working on a Django project and need recommendations for Python libraries
I’ve been using Python with Django for a while now, and I want to expand into machine learning and data analysis. There are so many options out there that I’m getting confused about which ones to pick.
Right now I’m considering these libraries:
Web scraping: Scrapy or requests with lxml
Machine learning: scikit-learn
Data analysis: pandas and numpy
Visualization: matplotlib
My main question is whether these choices make sense together, or if there are better alternatives. I need everything to work smoothly with Django since that’s my main framework.
Has anyone used similar combinations? I’m particularly interested in libraries that don’t require setting up separate frameworks. Just want something that integrates well and doesn’t overcomplicate my Django setup.
Any suggestions or experiences with these tools would be really helpful.
Your stack’s solid, but I’d swap pandas for polars on bigger datasets. Been running it in my Django projects and the speed boost is crazy - especially when processing data. Syntax is close enough that you won’t hate the switch. For charts, try bokeh if you want interactive stuff in your Django templates. Way better than matplotlib for web apps and handles live data updates without breaking a sweat. Wish someone told me about dask sooner - use it when your data won’t fit in memory. Works great with pandas and scikit-learn, plus you can drop it into Django without rebuilding everything. For scraping, httpx beats requests hands down. The async support crushes it for bulk jobs and the API’s basically the same. Just don’t forget rate limiting in Django or you’ll get banned fast.
Been running similar setups for years and your picks are solid. One swap I’d make - drop matplotlib for plotly if you’re showing visualizations in your Django frontend. Interactive charts crush static ones in web apps, plus the API’s cleaner.
For ML, stick with scikit-learn to start. Covers 90% of what you need and the docs are great. When you outgrow it, add xgboost for boosting.
Here’s what I learned the hard way - never run heavy ML training in Django views. Set up Redis with RQ (way lighter than Celery) for background jobs. Your site won’t freeze during model training.
Throw jupyter in your environment for prototyping. Notebooks beat everything for data experiments before writing Django code.
Last tip - use python-decouple for ML model paths and API keys. Keeps things clean and separate from Django settings.
Your library choices are solid - I’ve used a similar stack on several Django projects. Add seaborn alongside matplotlib. It works great with pandas dataframes and creates cleaner statistical plots with way less code. For web scraping, I actually prefer requests + BeautifulSoup over lxml for simple stuff. It’s more readable and easier to debug when things break. Save Scrapy for heavy-duty crawling. For Django integration: separate your ML/analysis work into Django management commands instead of cramming everything into views. This prevents timeouts and makes it easier to schedule tasks with Celery later. Consider adding joblib for model persistence. It beats pickle for sklearn models and handles large numpy arrays way better. Your whole stack integrates smoothly with Django if you structure the code right.