Module 2: Python Development Environment
The right tool for the right job — Choose the Python programming environment that suits you
Chapter Overview
The first step in learning Python is not rushing to write code, but choosing the right development environment. This chapter will introduce you to three mainstream Python programming environments, from beginner-friendly interactive notebooks, to powerful IDEs for professional developers, to zero-configuration cloud platforms.
Learning Objectives
After completing this chapter, you will be able to:
- Understand the pros, cons, and use cases of different Python environments
- Proficiently use Jupyter Notebook for data analysis
- Configure VS Code as a professional Python IDE
- Use AI programming assistants (Copilot, Cursor, Claude) to boost efficiency
- Flexibly use cloud environments (Colab, Kaggle) for collaboration and GPU computing
- Select the most appropriate tool based on task requirements
Chapter Contents
01 - Jupyter Notebook Quick Start
Target Audience: Beginners, data analysts, researchers
Core Content:
- Interactive programming environment basics
- Jupyter shortcuts and magic commands
- Complete academic research workflow (data cleaning → analysis → export LaTeX tables)
- JupyterLab extensions and advanced tips
- Parallel computing, progress bars, performance profiling
- Debugging techniques (pdb, error tracking)
Why Choose Jupyter?
- Write and see results immediately, as intuitive as Stata
- Mix code, charts, and text explanations (similar to R Markdown)
- Ideal for exploratory data analysis (EDA) and prototyping
- Academic mainstream: convenient for generating paper figures
02 - VS Code Setup Guide
Target Audience: Advanced learners, software developers, large projects
Core Content:
- VS Code installation and Python extension configuration
- Intelligent code completion (IntelliSense) and debugger
- Interactive Window (use Jupyter within VS Code)
- Project organization and workspace management
- AI Programming Assistants Landscape:
- GitHub Copilot (real-time code completion)
- Cursor (AI-native IDE)
- Claude Code (specialized for data analysis)
- Google AI Studio (free Gemini Pro)
- Aider (open-source CLI tool)
- Comprehensive shortcuts and best practices
Why Choose VS Code?
- Most powerful free IDE
- Supports multi-file management, Git integration, powerful debugging
- AI programming assistants make coding easier
- Ideal for writing reusable Python scripts and packages
03 - Online Python Environments
Target Audience: Everyone (especially for GPU needs or collaboration scenarios)
Core Content:
- Google Colab: Free GPU, Google Drive integration
- Kaggle Notebooks: 30h/week free GPU, rich datasets, data competitions
- Other Platforms: Paperspace, SageMaker Studio Lab, Binder, Deepnote
- In-depth cloud platform comparison (resources, features, pricing)
- Academic paper reproducibility workflow (using Binder)
- GPU usage and cost optimization strategies
- Data security and privacy protection
Why Choose Online Environments?
- Zero configuration, ready to use in browser
- Free GPU for deep learning
- Code anywhere (mobile, tablet compatible)
- Team collaboration and code sharing made easy
Three Environments Comparison
| Dimension | Jupyter Notebook | VS Code | Online Env (Colab/Kaggle) |
|---|---|---|---|
| Learning Curve | ⭐⭐⭐⭐⭐ (Easiest) | ⭐⭐⭐ (Medium) | ⭐⭐⭐⭐⭐ (Easiest) |
| Interactive Analysis | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Large Projects | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ |
| Code Completion/Debug | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| GPU Support | Need local hardware | Need local hardware | Free provision |
| Collaboration | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Installation Setup | Need Anaconda | Need VS Code install | Zero config |
How to Choose?
By Learning Stage
Week 1-2 (Beginner):
- This website's Python environment (simplest)
- Or Google Colab (online, no installation needed)
Week 3-4 (Intermediate):
- Install Jupyter Notebook locally
- Start running more complex analyses in Colab
Week 5+ (Professional):
- Configure VS Code
- Flexibly switch environments based on tasks
By Task Type
| Task | Recommended Environment | Reason |
|---|---|---|
| Learning Python Basics | This website / Jupyter | Interactive, instant feedback |
| Data Analysis (EDA) | Jupyter Notebook | Explore and document simultaneously |
| Regression Modeling | Jupyter Notebook | Convenient for generating tables and charts |
| Machine Learning | Jupyter / Colab | Develop in Jupyter, train in Colab |
| Deep Learning | Google Colab / Kaggle | Free GPU |
| Large Projects (Multi-file) | VS Code | Code completion, debugging, Git |
| Team Collaboration | Colab / Deepnote | Real-time sharing |
| Paper Replication | Jupyter + Binder | One-click execution |
By Research Scenario
Typical Social Science Research Workflow:
1. Data Cleaning (Jupyter Notebook)
↓
2. Exploratory Analysis (Jupyter Notebook)
↓
3. Write Reusable Functions (VS Code)
↓
4. Main Regression Analysis (Jupyter Notebook)
↓
5. Robustness Checks (Jupyter Notebook)
↓
6. Generate Paper Tables and Figures (Jupyter → LaTeX/PDF)
↓
7. Code Replication (GitHub + Binder)Learning Recommendations for This Chapter
Minimal Learning Path (Recommended)
If time is limited, follow this priority:
Must Learn (Week 1):
- 01 - Jupyter Notebook basic operations
- 03 - Google Colab quick start
Important (Week 2-3):
- 01 - Jupyter magic commands and advanced tips
- 02 - VS Code basic configuration
Enhancement (Week 4+):
- 02 - AI programming assistants (Copilot/Cursor)
- 03 - In-depth cloud platform comparison and cost optimization
Practice Recommendations
- Don't overthink choices: Beginners start with Jupyter, learn VS Code when proficient
- Hands-on practice: Complete all exercises in each article
- Find your rhythm: Use Jupyter for data analysis, VS Code for writing functions
- Leverage AI: But beginners shouldn't over-rely in first 3 months
- Keep it simple: Master one tool first, then expand to others
Next Steps
After completing this chapter, you will master:
- Jupyter Notebook: Interactive data analysis
- VS Code: Professional development environment + AI programming assistants
- Cloud Platforms: Free GPU and team collaboration
In Module 3, we will start learning Python basic syntax and officially enter the programming world!
Ready? Choose your favorite environment and let's start the programming journey!