Python Modules & Functions

1. About this Course

1. Author: Cornell University
2. Type: University Lecture Slides
3. Language: French
4. License: Cornell University Academic Materials

2. Prerequisites

1. Basic understanding of Python programming
2. Knowledge of variables and data types
3. Familiarity with basic control structures
4. Understanding of simple input/output operations

3. Target Audience

This material targets Cornell University students enrolled in CS1110: Introduction to Computing Using Python. It's designed for beginning programmers learning fundamental computer science concepts and Python programming skills.

4. Hardware and Software Tools

4.1 Hardware Tools

1. Standard computer or laptop
2. Sufficient memory for Python development
3. Reliable internet access

4.2 Software Tools

1. Python 3.x interpreter
2. Python development environment
3. Text editor or IDE
4. Web browser for course materials

5. Application Fields

1. Software Development
2. Scientific Computing
3. Data Analysis
4. Automation Scripting
5. Academic Research

6. Career Opportunities

1. Software Developer
2. Data Analyst
3. Research Programmer
4. Automation Engineer
5. Technical Analyst

7. Short Description

This Cornell University lecture covers Python functions and modules, teaching students how to create reusable code components and organize programs effectively using modular programming principles and Python's module system.

8. Detailed Course Description

This comprehensive lecture material from Cornell University's CS1110 course provides a thorough introduction to Python functions and modules, essential concepts for writing organized, reusable, and maintainable code. The course begins by establishing the fundamental purpose of functions as mechanisms for code reuse and abstraction, allowing students to break down complex problems into manageable pieces.

Students learn the complete process of function definition and invocation, starting with the basic syntax using the def keyword. The material covers different aspects of function parameters, including positional arguments, keyword arguments, and default parameter values. Special attention is given to understanding how arguments are passed in Python and the implications for function design. The concept of return values is explored in depth, including how to return multiple values using tuples and the proper use of the None value.

A significant portion of the lecture focuses on variable scope, explaining the differences between local, global, and built-in scopes. Students learn how Python resolves variable names using the LEGB (Local, Enclosing, Global, Built-in) rule and the importance of understanding scope for writing correct programs. The material includes practical examples demonstrating common scope-related pitfalls and best practices for avoiding them.

The course then transitions to modules and Python's import system, teaching students how to organize code across multiple files. Students learn different ways to import functionality, including import, from-import, and import-as statements. The lecture covers how to create and use custom modules, as well as how to leverage Python's extensive standard library modules. Practical examples demonstrate importing and using popular standard library modules like math, random, and datetime.

Throughout the material, emphasis is placed on good software engineering practices, including writing clear function documentation with docstrings, choosing meaningful names for functions and parameters, and designing functions with single, well-defined purposes. The course includes numerous coding examples and exercises that reinforce the concepts through practical application, helping students develop the skills needed to write modular, well-structured Python programs that are easier to debug, test, and maintain.

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