Inheritance of Classes: Fundamentals of Class Inheritance in Python's Object-Oriented Programming
Python class inheritance is a core feature of object-oriented programming, enabling the reuse of parent class attributes and methods while extending functionality by creating subclasses. Its primary goal is to address code redundancy and implement reuse, extension, and structural simplification. Basic Syntax: First, define a parent class (e.g., `Animal` with a `name` attribute and an `eat` method). A subclass (e.g., `Dog(Animal)`) inherits all attributes and methods of the parent class through inheritance and can additionally define new methods (e.g., `bark`). For example, an instance of `Dog` can call both the parent class method `eat` and the subclass method `bark`. Method Overriding: A subclass can define a method with the same name to override the parent class. For instance, `Dog` overrides the `sleep` method, using `super().sleep()` to invoke parent class logic. Python supports single inheritance (common, e.g., `class Dog(Animal)`) and multiple inheritance (with attention to method resolution order, MRO). The core roles of inheritance are reuse, extension, and clear structural organization, laying the foundation for polymorphism. Mastering syntax, method overriding, and the use of `super()` is key.
Read MoreBasics of Classes and Objects: Steps to Define a Class and Create Instances in Python
In Python, classes and objects are the core of object-oriented programming. A class is a "template" that defines attributes and methods, while an object is an "instance" created based on this template, with independent attributes for each instance. To define a class, use the `class` keyword, with the class name starting with an uppercase letter. The class body contains attributes and methods. The constructor `__init__` is automatically called to initialize attributes, where the first parameter `self` points to the instance, such as `self.name = name`. Instance methods must also include `self` as the first parameter, e.g., `greet()`. Objects are created by calling the class name with arguments (excluding `self`), like `person1 = Person("小明", 18)`, and each object has independent attributes. Attributes are accessed using `object_name.attribute_name`, and methods are called with `object_name.method_name()`, where `self` is automatically passed. Key points: A class is a template, an object is an instance; methods must include `self`; attributes and methods are separated. Mastering the process of "defining a class - creating an object - using the object" is sufficient to get started with Python OOP.
Read MoreNested Loops in Python: Use Cases and Considerations
Nested loops in Python are an advanced technique for handling multi - level repetitive tasks, referring to the situation where one loop is contained within another. The outer loop controls the overall scope, while the inner loop deals with the details. Its core scenarios include: 1. **Traversal of 2D data**: For example, a student grade sheet (a list of lists), where the outer loop iterates over students and the inner loop accumulates grades. 2. **Graph generation**: Printing regular graphs through nested loops, such as right - angled triangles (the outer loop controls the number of rows, and the inner loop controls the number of stars in each row) and rectangles. 3. **List combination**: Achieving full pairing of elements from multiple lists (Cartesian product), such as all element combinations of two lists. When using nested loops, the following points should be noted: Avoid having more than 3 levels of nesting (to reduce readability); ensure that loop variable names do not conflict; optimize performance when the data volume is large (such as using list comprehensions instead of simple nested loops); strictly indent the code; and clearly understand the scope of the break/continue statements (they only terminate the current inner loop). Reasonable use of nested loops can efficiently solve complex repetitive problems. However, it is necessary to balance readability and performance, and gradually master it by practicing basic scenarios such as the multiplication table.
Read MorePython Module Import: How to Use `import` to Introduce External Functions?
Python modules are .py files containing functions, variables, etc. Importing them allows reusing code to enhance development efficiency. Common import methods include: basic import `import module_name` (e.g., `import math`, with function calls requiring a module prefix like `math.sqrt`); renaming import `import module_name as alias` (e.g., `import math as m`); importing specific functionality `from module_name import function_name` (e.g., `from math import sqrt`); and importing submodules or custom modules (custom module names should not conflict with standard libraries). Avoid `import *` to prevent naming conflicts. For ImportError, check module paths and spelling. Proper use of imports makes code more concise and maintainable.
Read MoreAn Introduction to Object-Oriented Programming: A Simple Understanding of Python Classes and Objects
Object-Oriented Programming (OOP) centers on objects, decomposing problems into independent entities. Each object encapsulates attributes (features) and behaviors (methods), mirroring real-world observations. In Python, a "class" serves as a template for objects (e.g., a Car class), defined using the `class` keyword and containing attributes (variables) and methods (functions). The constructor `__init__` initializes attributes (e.g., color, speed), where the `self` parameter refers to the object itself, ensuring methods operate on the correct instance. Objects are instantiated via the class name (e.g., `my_car = Car("red", "Tesla")`), with each object having independent attributes. Attributes describe an object's characteristics (e.g., a car's color), while methods define its behaviors (e.g., driving). The core principle is encapsulation, which promotes modular and maintainable code.
Read MoreList Comprehensions: A Concise Python Technique for Creating Lists (Beginner-Friendly)
This article introduces Python list comprehensions as a concise method for creating lists, which replaces the traditional for loop combined with append in one line of code, making it more efficient and concise. The basic syntax is `[expression for variable in iterable]`, for example, generating squares of numbers from 1 to 10: `[i**2 for i in range(1,11)]`. Screening conditions can be added using `if`, such as filtering even numbers: `[i for i in range(1,11) if i%2==0]`. The expression supports flexible operations such as string processing (e.g., `name.upper()`) and function calls (e.g., `abs(num)`). It should be noted that list comprehensions use `[]` to generate complete lists, which consume memory; generator expressions use `()` to create lazy sequences, saving memory. The core advantages are concise code and high readability. It is recommended to practice rewriting traditional loop codes, such as generating cubes and filtering negative numbers.
Read MorePython Input and Output: A Practical Tutorial on print() and input() Functions
This article introduces basic input and output operations in Python, with the core being the `print()` and `input()` functions. The `print()` function is used to output content, supporting text, numbers, variables, or expressions. It allows customizing the separator (e.g., using `-` to separate elements) via the `sep` parameter and controlling the ending (default is a newline; setting it to an empty string enables multi-line content to be printed on the same line) through the `end` parameter. The `input()` function retrieves user input and returns it as a string, which needs to be converted to numeric types (e.g., `int()`/`float()`) for numerical operations. For multiple inputs, the `split()` method can be used to separate values by spaces or commas, etc. Taking a "Personal Information Collection Program" as an example, the article demonstrates combining these functions: obtaining name, age, and height, outputting formatted information, and calculating next year's age and height. The summary emphasizes that `print()` enables flexible output, `input()` requires type conversion, `f-strings` facilitate convenient variable and expression concatenation, and proficiency can be achieved through more practice.
Read MoreMust-Know for Beginners: A Detailed Explanation of Python Data Types (Integers, Strings, Booleans)
This article introduces Python's basic data types, using the analogy of "data boxes" with labels to help understand the operations of different data. There are three core types: 1. **Integer (int)**: Handles numbers (positive/negative/0), supporting addition, subtraction, multiplication, division, modulo operation (%), and integer division (//). It can be converted using int() (e.g., converting a string to an integer) and has no size limit. 2. **String (str)**: Text enclosed in quotes (single/double quotes, requiring matching pairs), supporting concatenation (+), length calculation (len()), and indexing (0-based). It can be converted using str() (e.g., converting an integer to a string). 3. **Boolean (bool)**: Only True/False, used for logical judgments, and supports the not operator for negation (e.g., in conditional statements). These three types are fundamental to programming. Subsequent learning will involve complex types like lists and dictionaries, making basic data types a crucial prerequisite.
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