List Sorting: The Difference Between Python's list.sort() and sorted()
In Python, the sorting tools `list.sort()` and `sorted()` have similar functions but essential differences. `list.sort()` is a list method that **modifies the original list in place** and returns `None`. In contrast, `sorted()` is a built-in function that **does not modify the original list** and returns a new sorted list. Both support the `reverse` parameter (to control ascending/descending order) and the `key` parameter (to define custom sorting rules), such as `reverse=True` for descending order or `key=lambda x: len(x)` for sorting by length. Application scenarios: `list.sort()` is suitable when the original list does not need to be preserved, while `sorted()` is preferred when the original list must be retained or when sorting other iterable objects like tuples or strings. The key distinction lies in whether the original list is modified and the return value; simply choose based on your requirements.
Read MoreFunction Parameters: An Introduction to Positional, Keyword, and Default Parameters
Python functions mainly have three basic parameter types: positional parameters, keyword parameters, and default parameters. Proper use of these can enhance the flexibility of functions. Positional parameters must be passed in the order defined in the function, and their number must match; passing fewer or more will cause an error. For example, `def add(a, b): return a + b`; calling `add(3, 5)` returns 8. Keyword parameters are passed using `parameter name=value`, and their order can be reversed, making them more intuitive and clear. When calling, positional parameters must come first, followed by keyword parameters (e.g., `greet(name="小明", message="Hello")`). Default parameters assign a default value to a parameter, which is used if the parameter is not provided during the call. They must be defined at the end of the parameter list after positional parameters. For example, `def circle_area(radius=3): return 3.14 * radius **2`; if radius is not passed, 3 is used by default. When using mixed parameters, the rules must be followed: positional parameters first, then keyword parameters; default parameters must be placed after positional parameters. In scenarios, positional parameters are used for key information, keyword parameters are suitable for multi-parameter situations, and default parameters are used for optional parameters where most values remain unchanged.
Read MoreFunction Definition and Call: How to Create Your First Function in Python?
Functions are a core tool for code reuse in Python, designed to solve the problem of repetitive code. By "packaging" functional modules, they save time and ensure consistency. The definition syntax uses the `def` keyword, including the function name, parameters (to receive external data), an indented function body, and `return` (to return a result, defaulting to `None`). When calling, parameters must be passed (either positional or keyword arguments), and the return value should be received. Key points to note include indentation requirements, parameter quantity matching, and unique function names. Mastering the basics of functions (parameters, return values) is crucial for advanced usage, as they allow breaking down complex logic and improving code simplicity and maintainability.
Read MoreLearning Python from Scratch: How to Use if-else Conditional Statements? A Practical Guide with Examples
The if-else conditional statement in Python is used to perform different operations based on conditions, addressing complex scenario judgment requirements (such as determining if a number is positive or negative, or whether a score is passing, etc.). Key syntax notes include adding a colon after the condition, using indentation to denote code blocks, and condition expressions that include comparison operators like >, <, and ==. Scenarios are categorized into three types: 1. Single condition execution uses if (e.g., checking if a number is greater than 5); 2. Binary choice uses if-else (e.g., determining if a score is passing); 3. Multi-condition sequential judgment uses if-elif-else (e.g., grade classification, where the first satisfied condition takes precedence). Key points to avoid: missing colons, indentation errors, improper condition order (e.g., checking lower scores first may prevent higher scores from being recognized), and using == instead of = for comparison operators. Once mastered, it enables flexible implementation of conditional judgment and is a core tool for Python's logical control.
Read MoreMastering Python Lists with Ease: Creation, Indexing, and Common Operations
Python lists are ordered and mutable data containers denoted by `[]`, where elements can be of mixed types (e.g., numbers, strings) and support dynamic modification. They are created simply by enclosing elements with `[]`, such as `[1, "a", True]` or an empty list `[]`. Indexing starts at 0, with `-1` representing the last element; out-of-bounds access raises `IndexError`. The slicing syntax `[start:end:step]` includes the start index but excludes the end index, with a default step size of 1. Negative step sizes allow reverse element extraction. Core operations include: adding elements with `append()` (to the end) and `insert()` (at a specified position); removing elements with `remove()` (by value), `pop()` (by index), and `del` (by position or list deletion); modifying elements via direct index assignment; checking length with `len()`, and existence with `in`. Concatenation uses `+` or `extend()`, and repetition uses `*`. Sorting is done with `sort()` (in-place ascending) or `sorted()` (returns a new list); reversing uses `reverse()` (in-place) or `reversed()` (iterator). Mastering list creation, indexing/slicing, and basic operations (addition, deletion, modification, querying, etc.) is essential for data processing.
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