Learnt about basic introduction of python programming from @saumyasingh

I learnt about python syntax,variables ,expressions and statements ,taking input from the user and looking forward to learn new things daily .

If someone has any suggestions then please drop in comments .

Automatic data structure visualization in your IDE using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵:

• Web Debugger binary tree demo

• VS Code setup video

An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 to visualize execution and reveals what’s actually happening.

An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 to visualize execution and reveals what’s actually happening.

Learn Python programming from scratch with interactive examples.

What You'll Learn

• Variables, data types, and operators

• Control flow: conditionals and loops

• Data structures: lists, tuples, dicts, sets

• Functions, modules, and packages

• File handling and error management

• Object-Oriented Programming (OOP)

• Advanced: decorators, generators, type hints

• Professional: testing, logging, virtual environments

Free course available here https://8gwifi.org/tutorials/python/

Hi everyone,

I'm an engineering student and I want to learn Artificial Intelligence and Machine Learning from the ground up. I already know some basic Python, but I'm looking for a structured, beginner-friendly video roadmap that can take me from the fundamentals to advanced topics.

I'm mainly looking for free YouTube courses or other free video resources that cover topics like:

Python for AI/ML

Mathematics (Linear Algebra, Calculus, Statistics)

Machine Learning

Deep Learning

Neural Networks

NLP

Computer Vision

MLOps (if possible)

Real-world projects

There are so many playlists online that I'm confused about which ones are actually worth following. If you had to start again today, which video courses or YouTube channels would you recommend, and in what order should I watch them?

I'm willing to invest several months in learning properly, so I prefer quality over speed.

Thanks in advance for your suggestions!

Learning data structures in Python gets easier with memory_graph visualizations. Data structures are no longer abstract concepts but concrete, clear and easy to debug.

This Hash_Map demo is a Python implementation similar to 'dict'. The demo visualizes:

• adding key-value pairs

• rehashing

• lookup by key

• iterating over keys

Understanding a data structure like linked list in Python is a lot easier when you can just see it: Linked_List demo

memory_graph visualizes Python objects and references, so data structures stop being abstract and become something you can debug with ease. No more endless print-debugging. No more stepping through 50 frames just to find one sneaky reference/aliasing mistake.

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening:

• Solution

• Explanation

• More exercises

It's instructive to compare with this earlier exercise (tuple with lists, instead of list with lists).

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening:

• Solution

• Explanation

• More exercises

The classic Index the Values using a dict problem for beginners visualized using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵.

Many algorithms can be easier understood after step-by-step visualization using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵. Here's a Selection Sort example.

• Try it on your own code: memory_graph

• See the VSCode setup video.

Pick the right way to “𝐂𝐨𝐩𝐲” in Python, there are 4 options:

𝚒𝚖𝚙𝚘𝚛𝚝 𝚌𝚘𝚙𝚢

𝚍𝚎𝚏 𝚌𝚞𝚜𝚝𝚘𝚖_𝚌𝚘𝚙𝚢(𝚊):
    𝚌 = 𝚊.𝚌𝚘𝚙𝚢()
    𝚌[𝟷] = 𝚊[𝟷].𝚌𝚘𝚙𝚢()
    𝚛𝚎𝚝𝚞𝚛𝚗 𝚌

𝚊 = [[𝟷, 𝟸], [𝟹, 𝟺]]
𝚌𝟷 = 𝚊
𝚌𝟸 = 𝚊.𝚌𝚘𝚙𝚢()
𝚌𝟹 = 𝚌𝚞𝚜𝚝𝚘𝚖_𝚌𝚘𝚙𝚢(𝚊)
𝚌𝟺 = 𝚌𝚘𝚙𝚢.𝚍𝚎𝚎𝚙𝚌𝚘𝚙𝚢(𝚊)

• c1, 𝐚𝐬𝐬𝐢𝐠𝐧𝐦𝐞𝐧𝐭: nothing is copied, everything is shared

• c2, 𝐬𝐡𝐚𝐥𝐥𝐨𝐰 𝐜𝐨𝐩𝐲: first value is copied, underlying is shared

• c3, 𝐜𝐮𝐬𝐭𝐨𝐦 𝐜𝐨𝐩𝐲: you decide what is copied and shared

• c4, 𝐝𝐞𝐞𝐩 𝐜𝐨𝐩𝐲: everything is copied, nothing is shared

See it Visualized using memory_graph.

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening:

• Solution

• Explanation

• More exercises

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening:

• Solution

• Explanation

• More exercises

It's instructive to compare with this earlier exercise (tuple with list, instead of list with tuple).

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening:

• Solution

• Explanation

• More exercises

Hey everyone,

I've been thinking about doing a few one-on-one Python mentoring sessions outside of work and wanted to see if there's any interest.

I've been working with Python professionally for the past 5+ years, mainly building production backend systems with FastAPI. I've also been on the startup side of things, so I've spent a lot of time taking products from an idea to something real that people use.

A lot of learning resources teach you syntax, but not necessarily how software is actually built in production. Things like:

- structuring a project

- writing clean, maintainable code

- APIs with FastAPI

- databases

- authentication

- logging & debugging

- testing

- deployment

- working with Git

- and using Python to build AI-powered applications

If you're:

- just getting started with programming,

- switching into software engineering,

- or want to become comfortable building real Python projects,

I'd be happy to help.

The idea is to keep it completely personalized—1:1 sessions where we work toward your goals instead of following a generic course.

I'm just gauging interest for now. If this sounds useful, leave a comment or send me a DM with what you're trying to learn.

it would always anger me whenever i would get stuck on a topic while watching youtube lecture or during my JEE days the LMS lectures of my coaching

Doubts would come like an avalanche, the only possible solution was typing it down in the comments or asking my fellow (smarter than me) mates

I always felt a lingering need, that what if i had a person who knows the video lecture i am watching in and out, who is smarter than me who knows everything not just things taught inside the video but also beyond, and is available 24x7

With this goal i made gUrrT, a tutor to help me go through a video lecture.

It smartly samples, video frames and extracts audio transcripts, then use vlms to caption the key frames, storing everything in a vector database.

Converting a video into a searchable array

Your asked question makes a call to the vector database then sends all the context to an llm which with its existing knowledge base along with the new video context answers all your questions from the video beautifully.

so all you gotta is type in your queries regarding anything you did not understand that is spoken or written on the board by the instructor

just go ahead send the video lecture to gurrt and ask all your doubts without worrying about rate limits, video durations, low computationa power or a paywall.

gUrrT is free, built with love and a lot of open source

Data structures like Trie can in Python be easier understood and debugged after visualization using the memory_graph package. A Trie is a tree of dictionaries and can be used for things like word completion.

An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 to visualize execution and reveals what’s actually happening. It's instructive to compare with these earlier exercises:

• https://www.reddit.com/r/PythonLearning/comments/1ox5mjo/python_data_model_copying/

• https://www.reddit.com/r/PythonProjects2/comments/1qdm8yz/python_mutability_and_shallow_vs_deep_copy/

• https://www.reddit.com/r/PythonLearnersHub/comments/1qlm3ho/build_the_right_mental_model_for_python_data/

This online compiler let you visualise any data structure written in Python real time
https://8gwifi.org/online-compiler/

Hey .

I wanted to share a realization I had recently that helped me actually finish a Python project instead of abandoning it half-way. Hopefully, it helps other beginners who might be stuck.

I’ve been trying to build a price-tracking project for a few weeks. My grand plan was to write a scraper from scratch, grab product data across a few e-commerce sites, and then use Pandas to clean it and build some trend charts.

But I hit a massive wall during the scraping phase. Between dynamic JavaScript loading, IP blocks, and sites constantly changing their DOM elements, I was spending 100% of my time trying to bypass bot protections. I got incredibly frustrated because my actual goal was to practice my Python data manipulation skills, not to become a reverse-engineering/anti-bot expert.

I finally decided to change my approach: Stop trying to reinvent the wheel for every single step.

I decided to decouple the data gathering from the data analysis. I ended up using a visual web scraper I stumbled across called ThorData just to handle the annoying extraction part. I basically pointed it at the pages, let it deal with the proxies and JS rendering, and just exported a raw JSON file.

Once I had that JSON file saved locally, the Python magic could finally start.

Without showing a wall of code, instead of fighting Selenium timeouts, I spent the last few days actually learning how to:

• Parse deeply nested JSON structures into Pandas DataFrames.

• Use Regex in Python to clean up messy string data (like stripping out weird currency symbols and formatting).

• Handle NaN values correctly without just carelessly dropping entire rows.

• Group the data to calculate historical low prices for specific items.

The biggest lesson I learned: As beginners, we often try to do everything from scratch and get burned out. If your main goal is to learn Pandas or data visualization, it's totally fine to use a no-code/low-code tool for the data gathering part so you don't lose motivation.

Has anyone else experienced this? When you guys build side projects, do you insist on writing the scraper from scratch every time, or do you use external tools to bypass the extraction phase so you can focus on the core Python logic? Would love to hear your workflow!