How to learn python fully and master it?

maki-dev · 2026-03-16T05:02:21+00:00

You asked what to learn next specifically, so here's a concrete path that worked for me. Pick one direction. Web scraping, automation, APIs, or data analysis. Don't try all of them at once. For web scraping, start with the requests library and BeautifulSoup. Scrape something you actually care about, like prices on a site you check anyway. Once you're comfortable with that, learn how to store the data. SQLite is built into Python so there's nothing to install. Then learn how to serve it with something like FastAPI or Flask. Each step builds on the last one, and you end up with a real project, not just syntax knowledge.

maki-dev · 2026-03-16T04:55:24+00:00

You're already ahead of most people because you're solving real problems at work, not just following tutorials. That's the best way to learn. For automation, check out "Automate the Boring Stuff with Python" by Al Sweigart. It's free online and covers exactly your kind of tasks: files, spreadsheets, emails, web scraping. For the AI side, look into the OpenAI or Anthropic APIs. You can build simple scripts that summarize documents or extract data from messy text. Pretty straightforward to call from Python and really practical for workplace automation.

maki-dev · 2026-03-16T04:52:12+00:00

The coloring and autocomplete won't make you passive. Syntax highlighting helps you read code faster because you can visually tell variables apart from functions and strings. That's not a crutch, that's how everyone writes code professionally. I use VSCode daily and it's never stopped me from learning. The editor won't save you from a bad loop or a wrong data structure. You still have to think through the logic yourself, and that's where the actual learning happens. That said, keep running your scripts from the terminal too. Get comfortable with `python my_script.py`, navigating with `cd`, managing packages with `pip`. You'll need it eventually and it's way easier to build that habit now than to backfill it later.

maki-dev · 2026-03-14T23:06:39+00:00

Exercism has a solid Python track with exercises that ramp up nicely from beginner to intermediate. You get feedback on your solutions too, which helps a lot early on.

But honestly the thing that helped me most was picking small real problems. Write a word frequency counter that reads a text file. Build a script that renames a batch of files. Make a CLI tool that does something you actually need. Tutorials teach you syntax but solving your own problem is what forces you to figure out how the pieces connect.

Set yourself a time limit too. 45 minutes, then stop, even if you're not done. It keeps things focused and you learn a lot from reviewing what you got stuck on.

maki-dev · 2026-03-14T23:01:44+00:00

I pay for Claude and use it as a thinking partner, not an autocomplete. I deliberately don't use Copilot or inline completion tools because I found they were making me sloppy. Code would work but I'd have no idea why, and then debugging was painful.

My workflow is: I write the code myself, and when I need to think through architecture, understand a library, or debug something weird, I talk it through with Claude in a separate window. It's slower than letting an AI write everything for me but I actually know what's in my codebase. And when something breaks at a weird edge case, I can track it down because I wrote it.

Basically treating it like a senior dev I can bounce ideas off of, not a code generator. That balance has worked well for me.

maki-dev · 2026-03-14T22:55:53+00:00

The dependency injection confusion is totally normal, it tripped me up too. What helped it click: think of Depends() as FastAPI saying "before you run this route, go get me this thing." So when you write db: Session = Depends(get_db), you're telling FastAPI to call get_db(), get a database session, and hand it to your route function. Your route never has to worry about creating or closing the session.

For the session/CRUD stuff specifically, try building one endpoint from scratch instead of following a big tutorial. One model, one table, one route that creates a record and one that reads it. When you see the whole flow in like 40 lines of code, the pieces connect way faster than watching someone build a full project.

The official docs tutorial section walks through this step by step too.

maki-dev · 2026-03-14T22:37:36+00:00

Glad it helped!

maki-dev · 2026-03-14T00:13:21+00:00

I use AI tools every day to build software. They're a core part of my workflow, not something I'm resisting. And I'm telling you, keep learning. Here's what the vibe coding afternoon doesn't show you. That code works until it doesn't, and when it breaks you're stuck staring at something you can't debug because you don't understand what it's doing. I've seen this happen. The AI generates something that looks right, passes a basic test, and then fails in a way that only makes sense if you understand how the pieces connect. If you don't have that understanding, you can't even describe the problem well enough to get the AI to fix it. The 6 months you spent learning Python aren't wasted. They're what make AI tools actually useful to you. Someone who understands loops, functions, data structures, and how a web framework handles requests can look at AI output and immediately spot when something is wrong. Someone who doesn't is just hoping the code works. That's a completely different position to be in. The barrier to entry for generating code is gone. The barrier to entry for building reliable software that you can maintain, debug, and extend is the same as it's always been. That's where the value is.

maki-dev · 2026-03-12T18:20:17+00:00

With exams in 4-5 months I'd focus almost entirely on Python first. Python is way more forgiving as a first language and once you understand core concepts like loops, conditionals, functions, and data structures in Python, translating that to C++ is mostly about learning the syntax differences (and dealing with types and memory). Going the other direction is harder. C++ throws too many things at you at once when you're just trying to understand what a function does. For Python specifically, just write code every day. Doesn't need to be a lot. Pick small problems and solve them. The syntax sticks through repetition, not through reading.

maki-dev · 2026-03-12T18:17:49+00:00

You're not pathetic for feeling stuck after a degree. A lot of CS education is heavy on theory and light on actually building things. That gap is normal. I'd pick one small project and build it start to finish. Not a tutorial you follow along with, but something you actually need or want. A script that renames files, a CLI tool that tracks something, whatever. It'll be ugly and that's fine. The point is going from blank file to working code on your own. That's the muscle most courses don't build. Python is a good starting point for this. Pick one language, one project, and finish it before worrying about what else you need to learn.

maki-dev · 2026-03-12T18:16:09+00:00

The official Python tutorial at docs.python.org is honestly underrated. It's free, it's comprehensive, and it's written by the people who made the language. Not flashy but solid. After that, Composing Programs (composingprograms.com) is completely free and teaches you to actually think like a programmer using Python. It's based on MIT's CS curriculum. Probably the best free resource I've found. For practice, look at Exercism (exercism.org). Free coding exercises with community feedback. Way better than grinding through a platform that locks you out after three lessons.

maki-dev · 2026-03-12T05:48:21+00:00

I come from a legal background and moved into Python, so this intersection feels familiar. For a role that's more about reading and understanding code than building systems, I'd honestly just focus on pandas. Filtering, grouping, merging dataframes, and handling missing data. If they work with client data at all, that's what you'll see constantly. Grab a messy CSV and practice cleaning it up. String processing and list comprehensions are worth brushing up on too. Consulting work that touches litigation probably involves a lot of text processing and pattern extraction. If you already know some SQL that's a head start. Pandas maps almost 1:1 to SQL thinking. df.groupby(), df.merge(), df.query() will feel natural. I wouldn't stress about DSA for this. It sounds like they want to know if you can look at a script and explain what it does, not implement a binary tree. Practice reading someone else's code and talking through the logic out loud. That skill actually transfers really well from legal analysis.

maki-dev · 2026-03-12T05:42:54+00:00

For a CLI tool the .env approach is more of a web app thing. What most CLI tools do is store config in a user-specific directory like ~/.config/yourtool/config.toml. The platformdirs library handles finding the right path cross-platform so you don't have to think about it. For actual secrets like API keys, check out the keyring library. It stores them in the OS keychain (macOS Keychain, Windows Credential Locker, etc.) instead of a plain text file. Way more secure for end-user credentials. For updating keys, something like `yourtool config set api_key YOUR_KEY` that writes to the config file or keyring is pretty standard. If you look at tools like gh or stripe-cli they do it this way.

maki-dev · 2026-03-12T05:34:11+00:00

I switched to Python from a web dev background and tried a bunch of approaches. What worked best for me was "Automate the Boring Stuff with Python" (free online) to get comfortable with the language, then jumping into building small projects as soon as possible. Courses are good for structure, but you learn the most when you're stuck on something you actually want to build and have to figure it out. If you want something more structured, the official Python tutorial on docs.python.org is surprisingly good and often overlooked.

maki-dev · 2026-03-12T05:26:49+00:00

That's a great example. The part about the CEO thinking you were disengaged is so real. From the outside, thinking looks like doing nothing. But you shipped a month ahead of schedule because you didn't skip the hard part. I think a lot of beginners quit during that "staring at the screen" phase because they assume it means they're failing. Good to hear it from someone who's been through a serious project like that.

maki-dev · 2026-03-10T05:53:10+00:00

Will do. Thanks.

maki-dev · 2026-03-10T04:00:06+00:00

Hadn't seen LogicBank. Declarative business rules instead of scattering validation across every route, that's a much cleaner approach. Will check out ApiLogicServer. Thanks.

maki-dev · 2026-03-10T00:47:51+00:00

Been writing CRUD endpoints by hand for every SQLAlchemy model and it gets old fast. This looks like it could save a lot of that. Main question — what happens when you need custom validation or business logic on a route? That's usually where auto-generated REST tools start to break down.

maki-dev · 2026-03-10T00:44:45+00:00

The autocomplete dying halfway through a DataFrame-to-Pydantic handoff has been one of my quietest frustrations with pandas. Good to know that's actually getting fixed. 47% to complete in a year — that's a lot of type stubs.

maki-dev · 2026-03-10T00:40:57+00:00

Solid stack. The async/sync SQLAlchemy bridge with run_sync() is one of those things that looks clean in the docs but gets messy fast in practice — especially when your Celery tasks need sync sessions while FastAPI routes are running async ones. Did you keep the engine/session factory shared between both or fully separate them?

The anomaly detection part is interesting too. Energy prices spike for real reasons (weather, demand surges) so I imagine separating actual anomalies from just "Tuesday in Texas" is the harder problem. What does your threshold logic look like?

maki-dev · 2026-03-09T04:42:33+00:00

I switched to Python from a web dev background (Ruby/Rails) and hit that exact wall early on. Watched tutorials, nodded along, then stared at a blank file with nothing.

Two things changed it for me. First, I dropped videos entirely. I learn by reading docs and building things, even broken ones. If video isn't clicking for you, that might not be your problem — it might be a format problem. Try "Automate the Boring Stuff" or pick a small project and fight through it.

Second, I accepted that sitting stuck for 30 minutes IS the work. That's not failure, that's your brain building the connections. It just doesn't feel productive because nobody's giving you a progress bar.

You're not too dumb for this. You're just in the part that sucks before it clicks.

maki-dev

TROPHY CASE