Dynamically Typed vs. Statically Typed Languages 🔄↔️

Dynamically Typed vs. Statically Typed Languages 🔄↔️

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Dynamically Typed Languages 🚀

• Type checking happens at runtime. ⏱️
• Variables can hold values of any type and change types during execution. 🌈
• No explicit type declarations (types are inferred at runtime). 📝
• Examples: Python, JavaScript, Ruby, PHP.

Python

# Python (Dynamically Typed)
x = 10          # x is an integer
x = "Hello"     # Now x is a string (no error)
x = [1, 2, 3]   # Now x is a list

def add(a, b):
    return a + b

add(3, 5)        # ✅ Returns 8 (integers)
add("cat", "dog") # ✅ Returns "catdog" (strings)
add(2, "apples")  # ❌ Runtime TypeError (caught when executed)

Pros:

• Flexible and concise code. ✨
• Rapid prototyping. ⚡
• Duck typing support (“if it quacks like a duck, treat it as a duck”). 🦆

Cons:

• Runtime type errors. 🐞
• Requires extensive testing. 🧪
• Harder to optimize for performance. 🐢

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Statically Typed Languages 🔒

• Type checking happens at compile-time (before execution). ⚙️
• Variables have fixed types declared explicitly or inferred by the compiler. 🏷️
• Type mismatches cause compile-time errors. ❌
• Examples: Java, C++, C#, Go, Swift.

Java

// Java (Statically Typed)
int x = 10;      // x is permanently an integer
// x = "Hello";  // ❌ Compile-time error: incompatible types

// public int add(int a, int b) {
//     return a + b;
// }
// add(3, 5);        // ✅ Returns 8
// add("cat", "dog"); // ❌ Compile-time error: incompatible types

Pros:

• Early error detection (during compilation). 🐞
• Better performance optimizations. 🚀
• Enhanced IDE support (autocomplete, refactoring). 💡
• Self-documenting code. 📄

Cons:

• More verbose (requires type declarations). 🗣️
• Less flexible for rapid iteration. 🐢

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Key Differences 🆚

Feature	Dynamically Typed	Statically Typed
Type Checking	Runtime	Compile-time
Variable Types	Can change during execution	Fixed after declaration
Error Detection	Runtime exceptions	Compile-time failures
Speed	Slower (runtime checks)	Faster (optimized binaries)
Flexibility	High	Lower
Verbosity	Less code	More type annotations

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Hybrid Approach: Gradual Typing 🧩

Languages like TypeScript (JavaScript superset) and Python (with type hints) blend both approaches:

Python

# Python with Type Hints (Optional Static Typing)
def add(a: int, b: int) -> int:
    return a + b

add(5, 3)     # ✅ Valid
add("a", "b") # ⚠️ Mypy/flake8 warns, but still runs at runtime (flexibility)

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Why Python is Dynamically Typed 🤔

• Flexibility: Ideal for scripting, rapid development, and duck typing. 🦆
• Simplicity: Less boilerplate; focus on logic over types. ✍️
• Legacy: Designed for ease of use (beginners, data science, automation). 👶📊🤖

🔑 Rule of Thumb:

• Use static typing for large-scale systems, performance-critical apps. 🏗️
• Use dynamic typing for quick scripts, prototyping, or when flexibility trumps strictness. ⚡