Python Learning

🔍 10 Useful String Methods in Python 1. split() → Break text into pieces. 2. join() → Combine multiple strings. 3. replace() → Replace part of a string. 4. strip() → Remove extra spaces. 5. startswith() → Check prefixes. 6. endswith() → Check suffixes. 7. find() → Locate text. 8. count() → Count occurrences. 9. upper() / lower() → Change case. 10. capitalize() → Capitalize the first letter. These methods appear in almost every real-world Python project.

One of our members asked for a Python Book This book, Think Python, is an introduction to Python programming for beginners. It starts with basic concepts of programming; it is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression.

🚀 Python Time Complexity Cheat Sheet ✅ List • Access by index → O(1) • Append → O(1) • Insert at beginning → O(n) • Delete from middle → O(n) • Search (in) → O(n) Best for: Ordered collections where fast indexing matters. ✅ Dictionary (dict) • Lookup → O(1) • Insert → O(1) • Update → O(1) • Delete → O(1) Best for: Fast lookups using keys. ✅ Set • Add → O(1) • Remove → O(1) • Membership test → O(1) Best for: Removing duplicates and fast membership checks. ✅ Tuple • Access → O(1) • Search → O(n) Best for: Read-only collections that shouldn't change.

📖 Reading Python Error Messages Suppose you see this. TypeError: 'NoneType' object is not iterable Instead of guessing, break it down. TypeError → You're performing an operation on an incompatible type. NoneType → The value is None. not iterable → Python expected something it could loop over, like a list or tuple. A common cause: def get_users(): print("Loading users...") for user in get_users(): print(user) get_users() doesn't return anything, so it returns None by default. Python can't loop over None. When you see this error, ask yourself: "Which variable was supposed to contain a list but ended up being None?"

🐍 15 Python Built-in Functions Every Developer Should Know You don't always need another library. Python already ships with powerful built-in functions that can make your code cleaner, shorter, and faster. 1. enumerate() - Loop through items while automatically keeping track of their index. 2. zip() - Combine multiple lists together element by element. 3. map() - Apply the same function to every item in an iterable. 4. filter() - Keep only the elements that satisfy a condition. 5. sorted() - Return a new sorted list without changing the original. 6. any() - Returns True if at least one item is truthy. 7. all() - Returns True only if every item is truthy. 8. sum() - Quickly calculate the total of numeric values. 9. min() / max() - Find the smallest or largest value instantly. 10. len() - Count the number of items in any iterable. 11. set() - Remove duplicate values while creating a collection of unique items. 12. isinstance() - Check whether an object belongs to a specific type. 13. range() - Generate sequences of numbers efficiently. 14. reversed() - Iterate over data in reverse order without modifying it. 15. help() - Open the built-in documentation for almost any Python object. Learning these built-ins will make your code look much more "Pythonic" and save you from writing unnecessary loops.

A Japanese AI company called Preferred Networks has a mature open-source library for NumPy/SciPy calculations on GPUs. It's called CuPy 🚀. For massive datasets, it is often enough to replace a single line: import cupy as cp The same array operations can run on CUDA up to 100 times faster. What it can do: 🛠 Highly compatible with existing NumPy and SciPy code 📝 Dramatically reduces the need to rewrite code or learn new syntax 💻 Supports not only NVIDIA CUDA but also AMD ROCm architectures Keep in mind: → Only faster for massive arrays; small datasets will run slower due to CPU-to-GPU data transfer lag → Strictly bound by your physical GPU VRAM limits (can cause out-of-memory errors). → Covers most major math functions, but does not replicate 100% of NumPy/SciPy modules. The project is completely open-source and battle-tested since 2015 📂: https://github.com/cupy/cupy

📘 Biopython: Tutorial and Cookbook ✍️ Authors: Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczyński 🔗 Read Online #Python ──────────────────── 👉 @free_programming_books_bds 👈

The Unofficial Python Graph Gallery If you work with data, you already know the pain of making charts look decent in Python. You spend 5 minutes writing the logic to process your data, and then 45 minutes wrestling with matplotlib or seaborn trying to figure out why your labels are overlapping, how to change a specific hex color, or how to remove those ugly default borders. This repository completely solves that. Instead of just listing libraries, it is a massive, beautifully organized collection of hundreds of data visualization examples. 🔗 Link

Python List Exercises Guide

Python Web Scraping This learning path you’ll learn the core Python technologies and skills you need to build your own web scraper. Web scraping is about downloading structured data from the web and processing selected data. 👉 You should already be comfortable writing Python scripts 🔗 Learn Here

📦 The Difference Between a Package and a Module These terms get mixed up a lot. 📗A module is a single Python file. math.py 📚A package is a folder containing multiple modules. utils/ helpers.py parser.py formatter.py Think of it like this: 📖 Module = One book 📚 Package = An entire bookshelf

Python Lambda Function

📂 Understanding Python File Modes When opening a file, the mode determines what you're allowed to do. Mode Meaning r 👉 Read only w 👉 Write (overwrites existing file) a 👉 Append to the end x 👉 Create a new file rb 👉 Read binary files wb 👉 Write binary files r+ 👉 Read and write 👉 Using the wrong mode is one of the easiest ways to accidentally erase a file.

❌ Five mistakes almost every Python developer makes once 1️⃣ Giving a function a default value that's a list or dictionary. This one is sneaky because it works in your first few tests and then quietly breaks the moment the function gets called more than once because that default gets created a single time, not fresh on every call, and it silently keeps growing in the background. 2️⃣ Creating a bunch of small functions inside a loop that each reference the loop variable People expect each one to remember its "own" value from when it was created. They don't. They all end up referencing whatever the loop variable became by the time the loop finished, which is almost never what you wanted. 3️⃣ Comparing decimal numbers with a plain equals sign Computers don't store decimal math with perfect precision, so two numbers that should obviously be equal sometimes aren't, according to the computer. There's a proper "close enough" comparison built for exactly this. 4️⃣ Confusing a quick copy with a real copy A fast, shallow copy of something with nested lists or dictionaries inside still shares those inner pieces with the original change one, and you accidentally change both. A true independent copy needs a different approach entirely. 5️⃣ Catching every possible error with one generic catch-all It feels protective in the moment, but it also hides real bugs behind the same wall as the error you actually expected, and you lose the ability to tell them apart. None of these mean you're bad at this. Almost everyone hits each one exactly once, and then never forgets it.