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numbers = range(1, 11)
evens = list(filter(lambda x: x % 2 == 0, numbers))
squares = list(map(lambda x: x**2, evens))
print(f"Evens: {evens}, Squares: {squares}")Coding works best on desktop or with an external keyboard.
Filter and Map - Python Typing CST Test
Filter and Map — Python Code
Using map and filter functions with lambda expressions.
numbers = range(1, 11)
evens = list(filter(lambda x: x % 2 == 0, numbers))
squares = list(map(lambda x: x**2, evens))
print(f"Evens: {evens}, Squares: {squares}")Python Language Guide
Python is a high-level, dynamically typed, multi-paradigm programming language known for simplicity, readability, and massive ecosystem support. It powers web development, data science, machine learning, automation, scripting, backend systems, and more.
Primary Use Cases
- ▸Backend web development
- ▸Machine learning & AI
- ▸Data analysis & visualization
- ▸Automation & scripting
- ▸API development
- ▸Scientific computing
- ▸DevOps tooling
- ▸Cybersecurity scripting
Notable Features
- ▸Simple, readable syntax
- ▸Massive standard library
- ▸Dynamically typed
- ▸Extensive third-party ecosystem (PyPI)
- ▸Cross-platform
- ▸Strong scientific & ML libraries
Origin & Creator
Created by Guido van Rossum in 1991, inspired by ABC language with a vision of a simple, readable language for everyday programming tasks.
Industrial Note
Python dominates in AI/ML research, automation-heavy engineering teams, fast MVP prototyping, data-driven industries, hybrid cloud pipelines, ETL scripting, scientific computing, and large-scale analytical workflows.
Quick Explain
- ▸Python emphasizes clean syntax and developer productivity.
- ▸It supports procedural, object-oriented, and functional programming styles.
- ▸Used across data science, AI, web development, automation, and scripting.
Core Features
- ▸Object-oriented and functional support
- ▸Garbage-collected memory management
- ▸Interactive REPL
- ▸Rich built-in data types
- ▸Module/package system
- ▸Asynchronous programming (async/await)
Learning Path
- ▸Basics + syntax
- ▸OOP + modules
- ▸Web or data specialization
- ▸Async + frameworks
- ▸Advanced tooling
Practical Examples
- ▸Data analysis script
- ▸REST API with FastAPI
- ▸Machine learning model
- ▸Automation with Selenium
- ▸File system utilities
Comparisons
- ▸Easier than Java for beginners
- ▸More flexible than C++
- ▸Slower than Go/Rust
- ▸Stronger ML ecosystem than JavaScript
Strengths
- ▸Beginner-friendly
- ▸Huge ecosystem
- ▸Excellent for AI/ML
- ▸Fast development cycle
- ▸Great community support
Limitations
- ▸Slower execution than compiled languages
- ▸Weak mobile development ecosystem
- ▸GIL limits multi-threaded CPU performance
- ▸Runtime errors due to dynamic typing
When NOT to Use
- ▸High-performance embedded systems
- ▸Performance-critical computation
- ▸Mobile app development
- ▸Browser-based execution
Cheat Sheet
- ▸List comprehension: [x for x in arr]
- ▸Dictionary: {'a': 1}
- ▸Lambda: lambda x: x + 1
- ▸Async: async/await
- ▸Import: from module import X
FAQ
- ▸Is Python slow?
- ▸Slower than compiled languages but fast enough with optimizations.
- ▸Is Python good for ML?
- ▸Yes-it's the top ML/AI language.
- ▸Can Python run on web?
- ▸Yes via backends, not directly in browser.
- ▸Is Python good for beginners?
- ▸It’s the most beginner-friendly mainstream language.
30-Day Skill Plan
- ▸Week 1: Syntax & basics
- ▸Week 2: OOP + functions
- ▸Week 3: APIs + automation
- ▸Week 4: Pick a specialization
Final Summary
- ▸Python is a flexible, beginner-friendly language with a massive ecosystem.
- ▸It dominates AI/ML, automation, and backend development.
- ▸Its clarity, libraries, and community make it ideal for rapid development.
- ▸Despite performance limitations, it’s among the most versatile languages ever built.
Project Structure
- ▸src/ modules
- ▸requirements.txt / pyproject.toml
- ▸venv environment
- ▸tests/ folder
- ▸README + configs
Monetization
- ▸Freelance automation scripts
- ▸ML/AI engineering
- ▸Backend development
- ▸Data analysis consulting
Productivity Tips
- ▸Use type hints
- ▸Use virtualenv
- ▸Use list comprehensions
- ▸Profile code regularly
Basic Concepts
- ▸Variables & dynamic typing
- ▸Control flow
- ▸Functions & classes
- ▸Modules and packages
- ▸Lists, tuples, dicts, sets
- ▸Error handling
Official Docs
- ▸Python Official Documentation
- ▸PyPI Package Index
- ▸Python PEP Index