How to Quickly Get Started with Python from Scratch?

1. Learning Path Planning

‌Define Goals and Direction‌Select a specific field based on interests or career needs (such as data analysis, web development, automation, etc.). General programming skills are the foundation for advancement.

1. ‌Environment Setup‌

• Install Python 3.x version and check <span>Add Python to PATH</span> to configure the environment variable.
• Select a development tool: beginners are recommended to use VS Code or Jupyter Notebook, and can switch to PyCharm after becoming familiar.

2. Core Basic Knowledge (2-3 weeks)

1. ‌Introduction to Syntax‌

   # Example: Guess the Number Game
   import random
   target = random.randint(1, 100)
   while True:
       guess = int(input("Enter your guessed number:"))
       if guess == target:
           print("Correct!")
           break
       elif guess > target:
           print("Too high")
       else:
           print("Too low")
   

• Variables and Data Types: Master operations and conversions of numbers, strings, lists, dictionaries, etc.
• Control Flow: Practice with “Guess the Number” and “Multiplication Table” using <span>if-else</span> and nested loops.

‌Functions and Modules‌

• Encapsulate reusable logic, learn about parameter passing, scope, and calling standard libraries (such as <span>os</span> and <span>datetime</span>).

‌Object-Oriented Programming (OOP)‌

• Understand concepts such as classes, inheritance, and polymorphism through the “Student Management System” case study.

3. Practical Skill Improvement (1-2 months)

1. ‌Small Projects to Accumulate Experience‌

• ‌Automation Scripts‌: Batch processing of Excel/PDF files.
• ‌Data Analysis‌: Use <span>Pandas</span> to analyze movie box office data and visualize it with <span>Matplotlib</span>.
• ‌Web Development‌: Build a simple blog system using <span>Flask</span>.

‌Using Third-Party Libraries‌

Direction	Recommended Libraries	Use Cases
Data Analysis	NumPy, Pandas	Data cleaning and statistical analysis
Web Scraping	Requests, BeautifulSoup	Web data collection
Machine Learning	Scikit-learn, TensorFlow	Model training and prediction

4. Advanced and Continuous Growth

1. ‌Participate in Open Source Projects‌Contribute code on GitHub or replicate classic projects to enhance collaboration and engineering skills.

2. ‌Algorithm Training‌Practice problems on platforms like LeetCode to strengthen logical thinking (recommended 1-2 problems daily).

3. ‌Standards and Optimization‌

• Follow PEP8 coding standards and use <span>flake8</span> to check code quality.
• Master exception handling (<span>try-except</span>) and debugging tools (such as breakpoints in VS Code).

5. Pitfall Avoidance Suggestions

• Avoid rote memorization; understand syntax logic through projects.
• Prioritize official documentation over scattered blogs.
• Practice coding for at least 30 minutes daily; after 3 months, you can independently complete practical programs.

‌Learning Path Summary‌:

Environment Setup → Basic Syntax → Functions/Modules → Project Practice → In-depth Focus → Continuous Improvement

When encountering problems, make good use of search engines (e.g., “How to connect Python to a database”), as 90% of issues have established solutions.