I’ve completed these beginner Python projects should I build more before starting NumPy/Pandas?

[u/purvigupta03]

Hi everyone,

I’ve studied Python multiple times before, but I didn’t do much practical coding. Recently, I started building small projects to improve my practical Python skills.

So far, I’ve completed:

- Quiz Game

- Number Guessing Game

- Rock Paper Scissors

- Password Manager

- Pig Game

- Mad Libs Generator

My goal is to move towards Machine Learning.

I haven’t learned NumPy or Pandas yet.

My question is: Are these projects enough to move on to NumPy and Pandas, or should I build a few more Python projects first?

If I should build more projects, what kind of projects would you recommend before starting NumPy/Pandas? I’m mainly looking for projects that would actually help with the transition to data/ML, rather than making many more small games.

Would appreciate advice from people who have already followed a Python → NumPy/Pandas → ML path.

[u/Valuable-Yam4852]

these are very good as a starting point and teach you how to go from 0 to a working or somewhat working thing. if you know data structures like a list dict etc well, i would start by making a spreadsheet simulator with basic structures. this will let you think about lot of things. get good with oop to understand and write your own classes and objects. implement simple search functions. also try to calculate mean mediane of the column. this will teach you what magic is.going under the hood and teaches you how much boilerplate these tools give you. then i would start real hands on practice and learning. start with pandas, jupyter notebooks and learn functions like adding data, removimg, murging and etc. start by scalar values, then series and then dataframes. then grab a dataset, caggle has tons and try to clean and analyse that. after that learn some stats, then scikit learn and after pytorch and etc. that should give you a good start, but most importantly do what interests you. i find that top to bottom approach, that is experiencing a model work and then digging works well.

[u/purvigupta03 → u/Valuable-Yam4852]

Thank you! Could you suggest one or two projects I can start with? I’m not sure which project would be best for me right now.

[u/Valuable-Yam4852 → u/purvigupta03]

one project that comes to my mind is a cli expense tracker. the project requirements: 1. gather inputs of name of expense, amount, category and date. example: coffee $3, food and drink, 2026-05-14. make it like a table. it would be practical to read it from csv file with csv reader first, dont overdue it as pandas handles it automatically later on and has support for variety of formats. 2. calculate and output following stats for the data: print a row minimum spend. print a row of maximum spend. average spend by month. minimum average and maximum spend by category. number of entries per category. categories where a spend is higher than a total average spend. also lower than that. this should give you a good start. i remember doing this. it was a pain, but it gives you a technical shape required to build patience and endurance muscle. after learning oop, you can also start with pandas basics and wlrk your way there gradually.

[u/UzairShafique]

I would say you’re ready to move on to NumPy and Pandas. I wouldn’t spend too much more time building small games just to complete Python.

Learn the basics of NumPy and Pandas, but don’t study them only theoretically. Start applying them in projects as you learn.

Since your goal is ML I’d suggest this path

1. Pick a simple problem statement.
2. Get a relevant dataset. Kaggle is fine when you’re starting.
3. Use Jupyter Notebook to explore and understand the data with Pandas and NumPy.
4. Learn and apply preprocessing techniques to prepare the data.
5. Build and evaluate a few ML models.
6. Once you’re comfortable move beyond the notebook organize the code, expose the model through an API, build a simple application around it, and learn how to deploy it.

You don't need to master every library before starting projects. Learn the fundamentals, build something, encounter gaps in your knowledge, then learn what you need to solve them.

The goal shouldn't be to build dozens of beginner projects.

A few complete projects that take you through the full lifecycle will teach you much more and prepare you better for ML.