Guide to High School Computer Science
  • 💻Introduction
    • windows & Python Development
    • macOS & Python Development
    • Visual Studio Code Settings
    • Set up Github
    • Author Page
  • 🧠Prerequisite Skills
    • Keyboard Typing
    • Files & Directories
    • Use of Command Line
    • Git & GitHub
    • Markdown
    • Starting Your Python Project
  • 🐍Python Programming
    • 🍎Python Basics
      • What is Python?
      • Procedural Programming & Programming Paradigms
      • String Formatting
      • Data Types
      • Input & Output to Console
      • Working with Numbers
      • Useful Built-in Functions
      • Math & Random Module
      • Boolean Data Object
      • Comparison, Logical, and Membership Operators
      • If Statements
      • Binary Decisions
      • Multiple Decisions
      • Nested Conditions
      • [EXTRA] Bitwise Operators
      • [EXTRA] Python Style Guide
    • ⏮️Iterations
      • Introduction to While Loops
      • Infinite Loop
      • Controlling Your While Loops
      • Introduction to For Loops
      • For Loops w/ Numeric Sequences
      • For Loops w/ Strings & Lists
      • Iterable Functions w/ For Loops
    • 📦Collections
      • Strings
        • String Basics
        • String Indexing
        • String Slicing
        • String Operators & Functions
        • Basic String Methods
        • String Methods Extended
        • String Methods Document
      • Tuples & Lists
        • Tuples
        • List Basics
        • List are Mutable
        • Adding Items to a List
        • Removing Items from a List
        • Search & Reverse a List
        • List Comprehension
        • List Methods Document
      • Sets
      • Dictionary
      • How to Store Multiple Data Items
    • 💡Defining Functions
      • Functions
      • print() vs return
      • Pre-determined Arguments
      • Nested Functions
      • Map & Filter
      • [Extra] Dynamic Arguments
    • 💾File I/O
      • How to Save Text to an External File
      • Reading CSV in Python
      • Reading JSON in Python
    • 🔨Basic Python Projects
      • Basic Calculator
        • Improving the calculator
        • Exercise Set 1
        • Exercise Set 2
        • 💎Streamlit Application #1
      • Basic Password Generator
        • Exercise Set 3
        • Exercises Related to Math
        • 💎Streamlit Application #2
      • A To-Do Task List
    • ⏳Introduction to Algorithmic Thinking
      • Big-O Notation
      • Basic Algorithms
        • Linear Search
        • Binary Search
        • Basic Sorting Algorithms
      • Recursion
      • Brute Force Algorithms
      • Greedy Algorithm
        • Time on Task (CCC 2013 J4)
        • Dijkstra’s Algorithm
      • Divide and Conquer
        • Merge Sort
      • Dynamic Programming
    • 🤯Object Oriented Programming
      • Class & Objects (Definitions)
      • OOP in Python
      • Encapsulation
      • Polymorphism
      • Inheritance & Overriding
      • Override Magic Methods
      • Case Study: 2D Vectors
      • Case Study: Deck of Cards
      • Exercise
      • Abstract Data Types
      • Case Study: Static 1D Array From Java
    • Competitive Programming
      • Is This Sum Possible?
        • Is the dataset sorted?
        • Searching for a value
        • Determine if the difference between an integer from the array and the target value exists
        • Sorting Algorithms
        • Using Two Pointers
      • Two Sum - LeetCode
        • Generate all possible pairs of values
        • Subtract each value from the target, see if the difference exists in the list
      • Longest Common Prefix - LeetCode
        • Compare all possible prefixes
        • Create the longest common prefix with the direct neighbour
      • Length of Last Word - LeetCode
        • Compare all possible prefixes
      • Where can I go from one point to another?
      • Sample Outline
    • IB Recipe Book
  • 💾Python & Databases
    • Intro to Databases & Data Modeling
      • Common Data Types in SQL
      • Introduction to ERDs
      • Primary Keys and Foreign Keys
      • Database Normalization
    • What is SQL?
      • Getting Started
      • SELECT Queries
        • Selection with Conditions
        • Selection with Fuzziness
        • Selection and Sorting in Order
        • Selection without Duplicates
        • Selection with Limited Number of Outputs
      • AGGREGATE Queries
        • Counting Rows
        • Sum, Average, Min/Max Queries
        • Working with Aggregate Queries
        • Power of using Groups
        • Exercise
      • Interacting with Multiple Table
      • Inserting Data
      • External Resource
  • ☕Java Essentials
    • Basics
      • Starting Java
      • Data & Variables
      • Handling User Inputs & Type Conversion
      • Arithmetic
      • IPO Model
      • Basic Built-in Methods
      • Exercise Questions
    • Conditionals
      • Boolean Operators
      • Compare Strings
      • If Statements
      • If Else Statements
      • Making Multiple Decisions
      • Using Switch
      • Flowchart Symbols
      • Exercise Questions
    • Iterations
      • While Loops
      • For Loop
      • Exercises
    • Java Type Casting
    • Strings
      • Common String Practices
      • String Formatting
      • Java Special Characters
    • Collection
      • Arrays
      • For Each Loop
      • ArrayList
      • Exercise Questions
    • Static Methods
      • (Aside) Clearing your Console
    • Randomness in Java
    • Delayed Output in Java
    • Java Output Formatting
    • Java Style Guide
  • 🛠️JavaScript Programming
    • Our Programming Editor & Workflow
      • Hello, world!
      • Commenting & Variables
      • Data in JavaScript
      • Operators
      • String Formatting
      • Getting User Input
    • JavaScript Exercise Set 1
    • Making Decisions
      • Comparing Values
      • Combining Boolean Comparisons
      • Creating Branches
    • JavaScript Exercise Set 2
    • While Loops
      • Infinite While Loop
      • While Loops and Numbers
      • While Loops and Flags
      • While loops w/ Strings
    • JavaScript Exercise Set 3
    • Subprograms & Functions
      • Creating a Function in JavaScript
      • Function with Input and Assignable Output
    • JavaScript Exercise Set 4
  • 💾Topics in CS
    • Computer Environments & Systems
      • Computer Components
        • In-depth Explanations
      • File Maintenance
      • Computer & Safety
      • Software Development
      • Bits & Binary
    • Careers related to Computer Science
    • Postsecondary Opportunities
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On this page
  • Using *args in your Function
  • Example: Sum of Numbers
  • Using **kwargs in your Functions
  • Example: A function that can display various dictionary combinations
  1. Python Programming
  2. Defining Functions

[Extra] Dynamic Arguments

In Python, *args and **kwargs are special features that provide flexibility when defining functions.

*args, short for "arguments," allows a function to accept a variable number of non-keyword arguments. This means you can pass any number of positional arguments to the function, and they will be collected into a tuple within the function. This is particularly useful when you want to create functions that can handle an arbitrary number of input values.

On the other hand, **kwargs, which stands for "keyword arguments," enables a function to accept a variable number of keyword arguments, which are collected into a dictionary within the function. This feature is handy when you want to work with named parameters and their values dynamically.

Using *args in your Function

*args is used to pass a variable number of non-keyword arguments to a function.

It allows you to call a function with any number of positional arguments, which are then collected into a tuple.

Example: Sum of Numbers

def sum_numbers(*args):
    result = 0
    for num in args:
        result += num
    return result

# Calling the function with different numbers of arguments
print(sum_numbers(1, 2, 3))  # Output: 6
print(sum_numbers(10, 20, 30, 40))  # Output: 100
print(sum_numbers(5))  # Output: 5

In this example, the sum_numbers function takes *args as its parameter. When you call the function, you can pass any number of arguments separated by commas. Inside the function, the arguments are collected into a tuple called args, and you can iterate through this tuple to perform operations, such as adding the numbers together in this case.

The key point here is that *args allows you to handle a variable number of arguments without having to specify each one individually in the function definition. It makes your code more flexible and adaptable to different input scenarios.

Using **kwargs in your Functions

**kwargs (short for "keyword arguments") is used to pass a variable number of keyword arguments to a function.

It allows you to call a function with any number of keyword arguments, which are then collected into a dictionary.

Example: A function that can display various dictionary combinations

def display_info(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

# Calling the function with different keyword arguments
display_info(name="Alice", age=30)
display_info(name="Bob", occupation="Engineer", city="New York")

In this example, the display_info function takes **kwargs as its parameter. When you call the function, you can pass any number of keyword arguments in the form of key=value pairs. Inside the function, these keyword arguments are collected into a dictionary called kwargs. You can then iterate through this dictionary to access and display the key-value pairs.

Here's the output for the above example:

name: Alice
age: 30
name: Bob
occupation: Engineer
city: New York

The key benefit of **kwargs is that it allows you to handle a variable number of keyword arguments without having to specify each one individually in the function definition. This makes your code more flexible and adaptable, particularly when you want to work with functions that can accept different sets of named parameters.

Together, *args and **kwargs empower you to write more versatile and reusable functions that can adapt to different situations and data structures, making your Python code more robust and expressive.

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Last updated 1 year ago

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