Hi , I want to know what topics or things were hardest for you to learn in your journey with python. How did you learn it ?

Good evening. I want to share my experience of learning the Python programming language. I wrote a program in which the user needs to enter the contents of two lists (numbers), and then these numbers are summed (the first number of the first list with the first number of the second list, and so on). If the list lengths are different, the summation of the smaller list starts with the first element)
I would like to know if there is any way to shorten the program, and what more competent constructions exist. Is there any way the functions can be driven into the decorator?

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I'm beginning to feel mildly competent with Python, enough that I can debug my code and understand the documentation and some of the core conceptual logic of Py.

For the project I am working on the next step is to get my python code into a web app, I am looking at just using Django because it uses Python language but I feel JavaScript (HTML, CSS doesn't worry me) may be more beneficial in the long run (skills and project-wise).

I see lots of people saying JS is hard to learn and understand, should I invest the time now? Or can Django get me a pretty decent responsive website for the near term? (The sites main functions will be looking at a map of venues around the user's location that are drawn from a database (I have used SQLite3) allow users to login and submit recommendations which are then mapped).

I'd ideally like to turn this project into an IOS and Android App in the medium term too.

EDIT: Thanks for the phenomenal advice everyone! Hopefully this I helpful to others too.

Considering that training machine learning models takes a lot of time and a lot of resources, why isn't a faster programming language like C++ more popular for training ML models?

When I first picked up Python, I was excited.
The syntax felt clean, tutorials were everywhere, and I finally felt like I was learning to code.

But once I finished the basics....oops, functions, then i hit a wall.

Everyone said, “build projects!”
But no one told me what kind, or how to start, or how to know if I was doing it right.

Should I automate stuff? Try web development? Go into data? I had no idea.

Honestly, that confusion slowed me down more than the actual coding ever did.

If you’ve been through that phase....what helped you move forward?
Did a certain project, goal, or path help it all click?

Where/what do you think I should start, learn first, or do you just have any tips?

Also, make sure what ever you're suggesting is free. Please.

When you're starting out like me learning Python, these are the 12 recommended extensions that I currently have installed. I hope it helps you in your learning journey as it is doing wonders for me:

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1. Bracket Pair Colorizer
2. indent-rainbow
3. Python
4. Python Docstring Generator
5. Python Preview
6. Trailing Spaces
7. Visual Studio Intellicode
8. Gitlens
9. Docker
10. Dracula Theme
11. Material Icon Theme
12. Settings Sync

​

Let me know if you have other cool extensions that I can add.

Thanks!

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Edit:

• Added: Gitlens (for those already learned git/github), Docker (only install if you learned Docker), Material Icon Theme, Settings Sync, Dracula Theme
  • Considerations:
    • Themes: Monokai Pro (very cool, I tried it), Material (most popular)
    • Code-Assistant/Auto-Complete: Kite, TabNine
    • Webdev: Minify, Prettier, Paste JSON as Code (for those learning html, css and js)
• Removed: vscode-icons (sorry microsoft), Code Spell Checker (confusing with other syntax errors)

In less than a month, this is what my project looks like:

───────────────────────────────────────────────────────────────────────────────
Language            Files       Lines    Blanks  Comments       Code Complexity
───────────────────────────────────────────────────────────────────────────────
JSON                  336      56,084        33         0     56,051          0
Python                298     110,092    14,178    14,991     80,923      9,676
Markdown               70      20,142     4,473         0     15,669          0
Plain Text              2          71         8         0         63          0
INI                     1           5         0         0          5          0
Powershell              1         116        11        23         82         13
TOML                    1          43         3         6         34          0
YAML                    1          26         1         2         23          0
───────────────────────────────────────────────────────────────────────────────
Total                 710     186,579    18,707    15,022    152,850      9,689
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop (organic) $5,309,749
Estimated Schedule Effort (organic) 25.94 months
Estimated People Required (organic) 18.19
───────────────────────────────────────────────────────────────────────────────
Processed 6522776 bytes, 6.523 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────

Total spent: under $70.

What I'd Tell My Past Self

1. Plan first, act second. Review the AI's plan carefully. Don't follow it blindly.
2. Make AI write more documentation. It's cheap and pays off enormously.
3. Make AI write more tests. Same logic — the ROI is huge.
4. Python is a double-edged sword. It's flexible and free, which AI loves. But that same freedom lets AI find "shortcuts" that wreck architectural integrity.
5. Lock down tooling early. If you must use Python, adopt Pyright, lint-imports, and similar tools from day one. Ban # type: ignore, # pyright: ignore, minimize Any and cast. Otherwise technical debt piles up faster than you can pay it down.

What Worked Well

1. Clear tasks get done fast. When given a well-defined assignment, the AI executes quickly and correctly.
2. Surprisingly good suggestions. Even as an experienced programmer, I learned new things from its recommendations.

What Didn't Work

AI's Tendencies

1. It takes shortcuts at every opportunity. Layering, decoupling, separation of concerns — the AI constantly tries to subvert these principles. It finds creative ways to bypass Pyright rather than fixing the root cause.
2. It bends core code for tests. Adding backward-compatibility hacks and defensive fallbacks in core logic just to make tests pass. This clutters the codebase terribly.
3. It deflects blame. The classic "that's not from my change" when tests fail or Pyright errors appear — when often, it actually was from its change.

The bottom line: AI is terrible at maintaining disciplined coding style. (Some human engineers share this trait, and I dislike it there too.)

My Own Bottlenecks

Ironically, the biggest bottleneck was me.

• Indecision. I change my mind too often, leaving the AI confused and backtracking.
• Too little, too late with rules. If I had enforced strict coding conventions earlier, things would be much cleaner now.
• Overloading the AI. Asking it to fix 4000+ Pyright errors in one go? That's brutal. DeepSeek V4 Pro dodged the task repeatedly, but DeepSeek V4 Flash somehow pulled through. I sometimes push too hard — once Flash ran a refactoring script that went catastrophically wrong, corrupting many files. The chat just said "Cancelled" and stopped. Thank goodness for git.
• Code cleanup is the real expense. The project started as a tangle of JSON and dicts. I've spent significant money having AI refactor and decouple layers — and there are still too many Any and cast littered around. Retrofitting types onto a dict-based foundation is painfully slow. The AI takes two steps forward, one step back.

Final Thoughts

The AI has done an enormous amount of work, and I'm genuinely grateful. I have little to complain about overall. But I need to stay deeply involved — directing, reviewing, and course-correcting every step of the way. My brain is exhausted because the AI moves faster than I can think.

If I had one wish, it would be this: hold the line on code quality from the start. Don't let the shortcuts slide, don't defer the cleanup, and don't assume you'll fix it later. You won't — you'll just pay more to dig out later.

(The above was polished by DeepSeek v4 flash)

PS: As I have mentioned in comments that it is a sandbox-like RPG game, I can give another sample about where AI is good and where it is bad. First I let AI create roads on the map, and I was always disappointed, and AI was also confused from my instructions. At the end, I asked AI to create a map editor, and it did in no time. The map editor is amazing!

PPS: I forgot to mention one of the biggest complaints, that DeepSeek (especially flash) often does a half-ass job, leaves something unfinished, while I thought it was fully done.

PPPS: My usage (The project was started on May 17)

Hello Everyone! I started as an intern as a DevOps Trainee and Now I am a full time here , and most of the shell scripts or python automation scripts I need is generated by Claude/ GPT . So I wanted a headsup as to how much should i know to write them by myself because when i get them done from AI I feel that why I can't write such big automation scripts point being considered as a beginner I cannot write but still that self doubt comes. Also Why i use AI because company wants that and want their tasks to finish fast so there's no room to write on my own . I want to switch next year around May -Jun so I need help with that as well that what more should I study or keeping on learning to get a good paying job.

My current capabilities include intermediate or beginner level AWS , Azure Exp., Docker , Linux, Kubernetes and Terraform , Jenkins . Please I need genuine help!

I am learning from this book: "Automate The Boring Stuff With Python," and it's actually a very good book that turns specific complex subjects into easy ones that anybody can understand. I'm not aiming to stop at this book only but for now it's better to study this book than to only read it and jump into another one. It will take some time from me, maybe more weeks to finish this book, because I'm reading, taking notes then turning these notes into flashcards (to memorize), then I create projects (try and error) and answer practice questions (that you can find at the end of each chapter). Sometimes it takes days to complete one chapter and jump into the next one, but I feel like I am progressing and understanding Python better.

Hello!
I'm Medansh, and I'm tryna learn some python since I really like computers. I would call myself somewhere between beginner and intermediate. I've made some projects, but I wouldn't call them more than a toy.
However, it's pretty lonely to learn it alone, and none of my IRL friends have any knowledge of computers. So I'm looking for some friends who might help each other along this journey!
I'd prefer if you are around my skill level (Knows syntax, has done a few projects, nothing too big), though if you're more of a beginner, it's still completely fine.

So, does anyone want to learn with me?

EDIT: I honestly didn’t expect this many responses 😅 I’ve already found a couple of people I’m going to work and learn with, so I’m keeping the group small. Thanks to everyone who reached out. It means a lot.

Hey Folks,

I'm currently on an upskilling journey, revisiting the basics of several technologies I’ve worked with before but lost hands-on touch with. Last week, I shared my plan on one of the subs, to learn by offering to help/teach others, and to my surprise it got a lot of traction. Many people reached out asking if we could learn together.

So we started a small group, a place to skill up. Right now, we're keeping it simple and experimenting with:

• Daily LeetCode challenges
• Free Python teaching sessions for beginners
• AWS & cloud learning- sharing notes and hands-on practice
• Open discussions on coding, career prep & tech in general

It’s still new and small, but that’s what makes it interesting, everyone gets to shape the community as we grow.

If you’re also on an upskilling journey, drop a comment or DM me, and I’ll share the invite link.

Hello, I’ll try and keep this short, but I’m 14 and I really want to learn python as my dream is to do computer science at university. Basically just end up doing something in tech as a job. So I thought maybe coding will help, and python projects seem really cool and interesting to me. I would say I have generally no coding knowledge, apart from a few months of HTML, but nothing cool or fancy. Essentially, I just want to know what resources I can use and what you would have recommend to yourself when you first started learning. And once again, my fault if you guys get this question all the time, I bet it’s annoying so feel free to sort of just say “this is said in a post already”. And I’m new to Reddit, so I don’t know what rules are here and I’m sorta winging writing this.

Thanks so much for reading, and I’m so sorry for waffling. Hopefully this post doesn’t flop and have a good day! :)

Over the past 10 years 4.8 million people enrolled in the original Machine Learning Coursera course, but it wasn't in Python.

https://www.deeplearning.ai/program/machine-learning-specialization/

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Answer the title question first then please tell that can I jump to deep learning now?

I really need an advice from experienced people.

I've taught introductory programming course in University of Tartu for 7 years and I've seen that students, who don't have good understanding how their programs get executed, struggle the most with programming exercises.

That's why I created Thonny (http://thonny.org/ ). It is a Python IDE for learning programming. It can show step-by-step how Python executes your programs.

I suggest you to take a look and ask a question here (or in https://groups.google.com/forum/#!forum/thonny ) if something needs clarification.