CodeExamples 🐍

A curated collection of Python code examples demonstrating practical programming concepts, performance optimizations, and real-world implementations. Perfect for developers looking to learn new techniques, compare approaches, or find reference implementations.

🌟 What's Inside

This repository contains 40+ Python examples covering:

• Performance Comparisons - See how different libraries and approaches stack up
• Algorithm Implementations - Classic data structures and algorithms
• Web Development - FastAPI, async programming, and HTTP patterns
• Database Integration - MongoDB, embeddings, and data processing
• Advanced Python - Context managers, generators, decorators, and more
• External APIs - Authentication, web scraping, and service integration

🚀 Getting Started

Prerequisites

• Python 3.13 (specified in .python-version)
• Individual examples may require specific packages (install as needed)

Quick Start

# Clone and explore
git clone <repository-url>
cd CodeExamples

# Run any example directly
python code/BloomFilter.py
python code/merge_intervals.py
python code/orjson_vs_json.py

📚 Featured Examples

🔧 Smart Context Managers

Perfect your resource management and API rate limiting:

# From context_manager_throttle_rate.py
from contextlib import contextmanager
import time

@contextmanager
def throttle(seconds):
    yield
    time.sleep(seconds)

# Usage: Never get rate-limited again!
for _ in range(5):
    with throttle(1):  # Pause 1 second between calls
        ping_api()

🎯 Elegant Algorithm Implementation

Clean, efficient interval merging:

# From merge_intervals.py
def merge_intervals(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    
    for interval in intervals:
        if not merged or merged[-1][1] < interval[0]:
            merged.append(interval)
        else:
            merged[-1][1] = max(merged[-1][1], interval[1])
    
    return merged

# Example: [[15,18], [8,10], [1,3], [2,6]] → [[1,6], [8,10], [15,18]]

🔍 Memory-Efficient File Processing

Handle massive files without breaking a sweat:

# From iterator_generator.py
def take_n(n, itr):
    result = []
    for i in range(n):
        try:
            result.append(next(itr))
        except StopIteration:
            break
    return result

# Process huge files efficiently
with open('my_huge_file.txt', 'r') as FILE:
    results = take_n(3, filter(find_my_pattern, 
                              (line for line in FILE)))

🛡️ Secure Password Handling

Production-ready password hashing with salt:

# From password_hash.py
import hashlib, os

password = "mypassword123"
salt = os.urandom(16)

# Strong password hashing
hashed = hashlib.pbkdf2_hmac(
    'sha256',                  # Hash algorithm
    password.encode('utf-8'),  # Convert to bytes
    salt,                      # Random salt
    100_000                    # Iterations (security)
)

🎨 Clean Enum Patterns

Make your code more readable and maintainable:

# From enums_examples.py
from enum import Enum

class UserRole(Enum):
    ADMIN = 1
    EDITOR = 2
    VIEWER = 3
    
    def can_edit(self):
        return self in {UserRole.ADMIN, UserRole.EDITOR}
    
    def can_delete(self):
        return self == UserRole.ADMIN

# Clean usage
if user_role.can_edit():
    print("User can edit content")

⚡ Performance Showdowns

Real benchmarks with actual numbers:

# From orjson_vs_json.py
import json, orjson, time

data = [{"id": i, "value": f"value-{i}"} for i in range(1_000_000)]

# Standard json
start = time.time()
json.dumps(data)
json_time = time.time() - start

# orjson
start = time.time()
orjson.dumps(data)
orjson_time = time.time() - start

print(f"orjson is {json_time/orjson_time:.2f}x faster!")
# Typical result: orjson is 2-3x faster!

🧮 Probabilistic Data Structures

Space-efficient membership testing:

# From BloomFilter.py
class BloomFilter:
    def __init__(self, n_items, fp_rate=0.01):
        self.m = -int(n_items * math.log(fp_rate) / (math.log(2)**2))
        self.k = max(1, int((self.m / n_items) * math.log(2)))
        self.bitarr = bytearray((self.m + 7) // 8)
    
    def add(self, item):
        for pos in self._hashes(item):
            self.bitarr[pos // 8] |= 1 << (pos % 8)
    
    def __contains__(self, item):
        return all(self.bitarr[pos // 8] & (1 << (pos % 8)) 
                  for pos in self._hashes(item))

# Test millions of items with tiny memory footprint!

🔥 Cython Performance

When Python isn't fast enough:

# From cyton_fast_sum.pyx
def fast_sum(long long[:] arr):
    cdef long long total = 0
    for i in range(arr.shape[0]):
        total += arr[i]
    return total

# Benchmark shows 10-50x speedup over pure Python!

🗂️ Example Categories

🏎️ Performance & Optimization

• orjson_vs_json.py - JSON serialization benchmarks
• numpy_vs_numexpr.py - Numerical computation comparisons
• list_comp_vs_manual.py - List comprehension vs traditional loops
• cyton_example.py - Cython performance testing
• profile_function.py - Code profiling techniques

🧮 Data Structures & Algorithms

• BloomFilter.py - Probabilistic data structure implementation
• Kth_largest_element.py - Efficient selection algorithm
• merge_intervals.py - Interval merging algorithm
• get_duplicates.py - Duplicate detection using Counter
• prime_numbers.py - Prime number generation

🌐 Web Development

• fastapi_dependency_injection.py - Clean DI patterns
• fastapi_htmx.py - Modern web interfaces
• async_threads.py - Concurrent request handling
• capture_headers.py - HTTP header processing

🔧 Python Patterns & Best Practices

• context_manager_*.py - Custom context managers
• iterator_generator.py - Memory-efficient file processing
• enums_examples.py - Clean enum usage
• patterns_usage.py - Design pattern implementations
• logging_with_extra_parameter.py - Advanced logging

🛡️ Security & Authentication

• password_hash.py - Secure password handling
• auth_by_pyseto.py - Modern token authentication

🗄️ Database & APIs

• openai_embeddings.py - Vector search with MongoDB
• mongo_shard.py - MongoDB operations
• get_youtube_transcript.py - API integration

🎯 Learning Paths

Beginner Path 🟢

Start with these fundamental examples:

1. merge_intervals.py - Learn algorithm thinking
2. get_duplicates.py - Understand data processing
3. enums_examples.py - Clean code patterns
4. iterator_generator.py - Memory efficiency

Intermediate Path 🟡

Level up with these examples:

1. context_manager_throttle_rate.py - Advanced Python features
2. fastapi_dependency_injection.py - Web development patterns
3. BloomFilter.py - Data structure implementation
4. password_hash.py - Security practices

Advanced Path 🔴

Master these complex topics:

1. async_threads.py - Concurrent programming
2. cyton_example.py - Performance optimization
3. openai_embeddings.py - AI/ML integration
4. patterns_usage.py - Design patterns

🏃‍♂️ How to Run Examples

Single Scripts

python code/BloomFilter.py
python code/merge_intervals.py
python code/orjson_vs_json.py

Web Applications

# FastAPI applications include uvicorn.run()
python code/async_threads.py
python code/fastapi_dependency_injection.py

# Then visit http://localhost:8000

Performance Benchmarks

# These show timing comparisons
python code/numpy_vs_numexpr.py
python code/list_comp_vs_manual.py
python code/orjson_vs_json.py

Interactive Examples

# Some examples require input
python code/password_hash.py
python code/login_to_upwork.py

📦 Dependencies

Most examples are self-contained, but some may require:

# For performance examples
pip install orjson numexpr numpy

# For web examples  
pip install fastapi uvicorn starlette

# For database examples
pip install pymongo requests

# For specific examples
pip install pyseto babel cython pydantic patterns

🤝 Contributing

Found a bug? Have a cool example to add? Contributions welcome!

• Keep examples focused and well-documented
• Include timing comparisons for performance examples
• Follow the existing naming conventions (snake_case)
• Add docstrings explaining the concept demonstrated

📖 More Resources

• Check WARP.md for development guidance
• Each example includes inline documentation
• Many examples include comparative analysis
• Look for related examples in similar categories

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Happy Coding! 🎉 Explore, learn, and build amazing things with Python.

"The best way to learn programming is by reading and writing code." - Start exploring! 🚀