I'm new to programming, and the Python tutorials I've found on YouTube don't explain things very well. Can you recommend the best YouTube channels to learn Python from scratch? to build a strong foundation that will help me with DSA later on.

I'm curious to hear from people who've been learning or using Python for a while. Was it loops, functions, OOP, recursion, decorators, or something else?

What finally made it "click" for you?

I think hearing real experiences can help beginners understand that struggling with a topic is completely normal.

Open-source learning project. If you spot something that can be improved, I'd love your feedback or a PR.

https://github.com/Samar-Upreti/Python_Projects

Program Description:

Windrose is a program for which its function is to provide the user with advice on which windows to open to achieve optimum airflow ventilation. The program receives the user's location and cardinal orientation as inputs from the user and uses that with information received from Open-Meteo's wind information to provide the user with advice on which windows to use to achieve the most optimum ventilation. 

Repo: https://github.com/OmarFishir/Windrose

Video Demo: https://youtu.be/OluDKioaCnY

This is my first programming course. I loved the CS50 format and how useful the problem sets have been. However, even after finishing this course, I feel like I've been training in a swimming pool then dropped into an ocean.

I want to do CS50x, CS50 AI, CS50 web, and CS50 Cybersecurity.

My goal? I don't really know. I just want to learn and be able to be competent in all of those fields until I find one to gravitate towards most. I think CS50x should be first?

The same tree represented in two very different ways visualized using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: Binary Trees

🔗 Binary Tree as Nodes: The tree is built out of multiple node objects. Each node stores its value and two references, one to its left child and one to its right child.

📦 Binary Tree as List: The tree is stored in a single list or array. Instead of references, indices represent the relationships between nodes. For a node at index, its children can be found using a simple calculation: - Left child: 2 * index + 1 - Right child: 2 * index + 2

So, when should you use which representation?

The node-based version makes the structure explicit and intuitive. It is great for education: students can clearly see how nodes are connected while practicing classes and references/pointers. It is flexible and works particularly well for irregular or sparse trees. It is the clearest representation when learning how trees work.

The list-based version can be very efficient for (nearly) balanced trees. It does not need to store child references, requires fewer separate memory allocations, and keeps values close together in memory for improved cache performance.

The same abstract data structure, but with very different trade-offs in clarity, flexibility, and performance.

Which representation would you use?

For some background on me, I am currently a loan review consulting manager and my day is spent reviewing commercial portfolios at banks for weaknesses and how they can improve.

I have always been a big fan of process improvements and work a lot with VBA to automate a lot of what I do. Ive noticed some of my larger projects I want to take on seem to be too much for VBA. And python seems to be more ideal to get the job done.

I find coding interesting and want to learn more about python. But I’m nervous that it won’t be very applicable to what I do full time. I still have future aspirations of creating a loan review software as a side hobby (I know it would take years) but I don’t want to prioritize learning python if it’s not very beneficial for my job. I don’t really have much free time with a toddler and infant.

Would anyone know how I could leverage it to justify the time I will spend?

built simple calculator

Hi everyone,

I'm looking for a Python practice platform/course for intermediate to advanced learners.

I have previous Python experience and have already worked on several Python projects, but I haven't used it intensively for around 3 years. I don't want to go through beginner material (syntax, basic exercises, "build a calculator" type of stuff).

What I'm looking for:

• practical, project-based learning
• realistic problems/tasks similar to real development work
• a structured platform or course (not just documentation or random tutorials)
• something that helps me refresh Python coding skills by actually building things

Bonus points if it includes modern topics like FastAPI, APIs, AI/LLM integration, automation, or data engineering-related projects.

Do you know any good platforms or courses that fit this level?

Thanks!

Not a trick question or LeetCode puzzle. What's a question that actually reveals whether someone understands Python?

I am a mechanical engineering student who doesnot even know how to write a line of code in any languages. I tried watching some videos on youtube but all that felt way boring. I need to learn coding in python nonetheless for a project. Could you suggest ways to ignite interest in the subject and make its study intuitive and enjoyable , maybe some book or other resources . Thanks

A modular, multi-page Command Line Interface (CLI) To-Do application built using Python. Designed with clean code practices, this project separates application logic across multiple files and implements file handling for persistent data storage.

#File:- (todo.py)

from todo import Todo
my_todo = Todo()
while True:
    user_choice = input("Enter 1 to add_task,2 to show_task,3 to exit: ")
    if (user_choice == "1"):
        my_todo.add_task()
    elif(user_choice == "2"):
        my_todo.show_task()
    elif(user_choice == "3"):
        break
    else:
       print("print only 1,2,and 3:--")



# File:-(loop.py)
import loop_py
class Todo():
    def __init__(self):
        self.tasks = []
        self.completed_tasks = "tasks.txt"
        
    def add_task(self):
        new_task = input("Enter some task:")
        self.tasks.append(new_task)
        with open("Data_base.txt","a") as f:
            result = f.write(new_task + "\n")
            print("Data successfully saved")
            f.close
    def show_task(self):
        if (not self.tasks):
            print("your list is emptied")
        else:
            for i,task in enumerate(self.tasks):
                print(i + 1, task)

Completed a multi-page To-Do app in Python with persistent file storage! 🐍 Now that I've mastered core Python basics and modular structure, my next goal is diving into FastAPI, relational databases (PostgreSQL), and building production-ready Backend APIs(Give me your feedback that what I do now.....)

in the old age the best way to learn something is to be with coach that walk with you step by step, in the new age that coach call AI and also free.

Make a gem on gemini, or gpt on chatgpt, or any specific chat for learning python and you will see fast progress.

My think, maybe I am wrong!

Hey everyone
A few weeks ago i took a 15h course on begginer phython, and they reccomended me to learn with CS50. I have done the first week problems wihtout any lectures.

i also had privous experience with phython but it was with my calculator making programs to do exercices for me

I want to know what's the best way to learn???

My parents have agreed to help me get a computer to start learning python. I need recommendations within a budget of around 400-500 dollars that run well. Are there any good options around that price? Also, if you have any advice on brand that would be appreciated.

The number one mistake I see beginner Python learners make is spending months on YouTube tutorials and then completely freezing up when they open a blank .py file to code on their own. It's called Tutorial Hell, and it's a real problem.

The difference isn't talent; it's having a structured roadmap that forces you to build things, not just watch things.

Here is the exact 4-phase breakdown I follow:

Phase 1: Foundations & Core Logic (Weeks 1–4)

Focus: Get comfortable with how Python actually executes code, not just what it does.

• Week 1: Python 3.12+ setup, VS Code with Black auto-formatter, terminal basics, REPL vs script files
• Week 2: Core data types (int, float, str, bool, None), dynamic typing, type casting, truthiness, match/case
• Week 3: if/elif/else, for and while loops, enumerate(), zip(), break/continue, and the for-else pattern
• Week 4: Functions, *args/**kwargs, the mutable default argument trap, LEGB scope rule

Hands-on drills: Terminal guessing game with random.randint(), a modular unit converter CLI with input validation.

Phase 2: Data Structures, Files & Error Handling (Weeks 5–8)

• Week 5: Lists, Tuples, Dicts, Sets, O(1) vs O(n) lookup, list/dict/set comprehensions
• Week 6: f-string format specifiers, string cleaning methods, regex with the re module
• Week 7: try/except/else/finally, custom exception classes, defensive coding patterns
• Week 8: Context managers, pathlib, csv.DictReader, json.load/dump

Capstone drill: A persistent JSON inventory manager with full CRUD from the command line.

Phase 3: OOP, Git & REST APIs (Weeks 9–12)

• Weeks 9–10: Classes, __init__, dunder methods, inheritance, super(), @ property decorators
• Week 11: Virtual environments, requirements.txt, professional project structure, Git workflow
• Week 12: HTTP protocol, the requests library, API authentication, generators with yield

Capstone drill: A banking system simulator with an account hierarchy, and a live weather CLI hitting OpenWeatherMap.

Phase 4: Portfolio Projects (Weeks 13–16)

This is where you build three production-grade projects for your GitHub:

1. CLI Log Intelligence Tool — argparse + pathlib + regex to parse Nginx logs and generate JSON/CSV reports
2. API Harvester & Alert Bot — polls a public API, stores historical data in SQLite, fires Discord webhooks on conditions
3. REST API with FastAPI — full CRUD backend with Pydantic validation, SQLite persistence, and a pytest test suite

The 3 Rules That Make This Work

1. The 70/30 Rule: 30% reading, 70% typing code by hand. Never copy-paste from examples. Muscle memory is built through repetition.

2. The 30-Minute Debug Rule: Before searching for the answer or asking for help, spend 30 minutes reading the traceback bottom-up, checking docs, and using print(). You will solve 80% of problems this way.

3. Ship Every Week: Every Friday, push code to GitHub; even if it's broken. Progress beats perfection.

Happy to answer questions about any specific phase or topic. What stage are you at right now?

Keeping it short and sweet 🔥

If you are a python tutor that create notebook for students then this is for you.

Cause my platform can take in a notebook and auto grade within your created class.

If you prefer more of creating exercises for students. That is covered too and they can execute within the platform, get graded and you review the code. I got you there tooo🔥…

Of course you can vibe code something similar but so long keep the student entertained😁.

Hi everyone,

I'm 24 years old with a non-tech background. I quit my previous job in 2024 and have been looking for my next step since. I took a basic programming class, covered Object-Oriented Programming (OOP) in Python, and have foundational knowledge in HTML, CSS, and basic JavaScript.

Initially, I aimed for Web Development, but the local market feels overwhelmingly competitive for entry-level roles. I want to narrow my focus and build a solid foundation in one area before applying for jobs, but I'm stuck between Data Analysis and Backend Development.

Hey

Quick question for the group.

I’ve been coding in Python professionally for 5+ years. Backend with FastAPI, some startup work too.

What I keep seeing is this: people learn loops, functions, OOP... and then hit a wall when they try to build and ship something real.

Because no one teaches the "in-between" stuff:

- Project structure

- Clean, maintainable code

- APIs, DBs, authentication

- Testing, logging, deployment

- Git

- Building AI features into apps

I’ve been toying with the idea of doing a few 1:1 mentoring chats on the side, just to walk through these things with people and help based on their specific goals.

Not selling anything, just wondering if that would actually help anyone here.

For those learning right now: what’s the hardest part for you? Is it the code, or is it figuring out how to turn it into a real project?

hi , i'm biggenr learn python , 80% of basics i learn it , actully i learn basics by MOOC Corse , now i'm looking for AI appliction i Want it Free like Gemini , Gemini now is useless for me i'dont know why and i'm asking for something i need avices about my learn methode i use AI as teacher , But as I learn to understand coding, I feel discouraged by what some people say—claims that programming relies entirely on AI, and things like that

When I started learning Python, I discovered that sometimes the vocabulary intimidated me more than the code itself.

My first language is Spanish, so learning programming also meant becoming familiar with English words, abbreviations and combinations that may seem completely obvious to experienced programmers but were not obvious to me as a beginner.

For example, concatenation sounded like an advanced operation. Then I learned that, in a simple case, it can mean joining strings together:

first_name = "Guido"

last_name = "van Rossum"

full_name = first_name + " " + last_name

print(full_name)

Another small discovery happened when I learned len(). At first, it was simply a Python function whose name I had to memorize. When I realized that len was short for length, the name suddenly explained exactly what the function did:

word = "Python"

print(len(word))

The same thing happened with elif. I initially saw it as another strange Python keyword. Later, I learned that it comes from else if, and its purpose immediately made more sense:

age = 15

if age >= 18:

print("Adult")

elif age >= 13:

print("Teenager")

else:

print("Child")

Other names are more direct once you know their English meaning. Functions such as min() and max() already give you a clue about what they return. enumerate() also became easier to remember when I connected it with the idea of numbering items while going through them:

names = ["Ana", "Luis", "Carlos"]

for number, name in enumerate(names, start=1):

print(number, name)

Indentation was another term that sounded more complicated than it was. It refers to the space at the beginning of a line, similar to indentation in ordinary writing. The important difference is that, in Python, it is not only decorative: it defines which lines belong to a block of code.

I am not saying that every programming concept is easy or that these short explanations cover everything. I am still learning myself. My point is that an unfamiliar word can sometimes create a bigger mental barrier than the basic idea behind it.

Now, whenever I encounter a new term, I ask:

1. What does it mean in plain language?
2. Does its name or abbreviation come from another word?
3. What is the smallest example that demonstrates it?
4. What problem does it help solve?

What programming term, keyword or function sounded much more complicated than it actually was when you first heard it? What explanation finally made it click for you?

I'm learning Python for a career in data analytics through YouTube. So far I've covered:

Variables and data types

Operators

If-else statements

Loops

Functions (arguments and return values)

Lists, tuples, sets, and dictionaries

Indexing and slicing

Random module

Time module

Basic math operations

At this point, should I move on to NumPy, or are there any core Python topics I should learn first?

My goal is to become a Data Analyst, so I'd appreciate advice on what topics are essential before starting libraries like NumPy, Pandas, and Matplotlib.

Just completed my class 12 and in free time I was thinking to start some coding but I don't know if it is really helpful for me because I took BTech computer science with specialisation in cyber security

Hi everyone,

Today I published my first Python package to PyPI as part of my Python learning journey.

The project is called redfishLiteApi, a lightweight command-line tool for interacting with Redfish management APIs commonly used in server management environments.

While building this project, I learned quite a bit about:

- Python packaging

- Building command-line tools

- Unit testing

- GitHub Actions CI/CD

- Automated PyPI publishing

Current features include:

✅ GET / POST / PATCH / DELETE support

✅ Basic Authentication and Redfish Session Login

✅ Recursive Redfish resource discovery via u/odata.id

✅ Search for specific fields in JSON responses

✅ Display API paths for matching fields

✅ Export API responses to files

✅ Install directly from PyPI

Installation:

```bash

pip install redfishLiteApi