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

📘 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
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👉 @free_programming_books_bds 👈

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

🐍 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.

📖 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?"

🚀 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.

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.

🔍 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.