Is Python a Statically or Dynamically Typed Language? What's the Difference?

Python is a dynamically typed language. This means developers do not need to specify what type of data a variable will hold when creating it. Instead, Python determines the type during runtime as the code executes. In contrast, statically typed languages such as C require developers to declare the type of every variable upfront, before the code runs.

What is Dynamic Typing?

With dynamic typing, any value can be assigned to a variable, and Python automatically determines its type:

# Python (dynamically typed)
name = "Alice"           # This is a string
name = 42                # Now it is an integer - no problem
name = [1, 2, 3]         # Now it is a list - still acceptable
name = 3.14              # Now it is a float - no error

print(type(name))        # Output: <class 'float'>

The interpreter determines what type each variable holds as it encounters it. Developers have complete freedom to change a variable's type at any point.

What is Static Typing?

With static typing, a variable's type must be declared when it is created. Once declared, that variable can only hold values of that specific type:

// C (statically typed)
int age = 25;           // This is an integer
age = "Alice";          // COMPILATION ERROR - compilation stops here

The compiler checks all type declarations before the program runs. If there is a type mismatch, compilation fails and the program cannot start. The compilation process stops immediately at the first error, so subsequent lines are never reached or evaluated.

The Key Difference: When Are Errors Caught?

This is the fundamental distinction between the two approaches:

Statically typed languages (C):

• Type errors are caught during compilation (before the program runs)
• If there is a type mismatch, the code fails to compile and the program cannot run
• Errors are prevented before deployment to production

Dynamically typed languages (Python):

• Type errors are caught during runtime (while the program is running)
• The program starts successfully, but crashes when execution reaches a line with incompatible types
• Errors are only discovered when that specific code path is executed

When Does the Error Appear? A Practical Example

With a statically typed language like C, a type error prevents the entire program from running:

// C (statically typed)
int age = 25;
age = "Alice";          // COMPILATION ERROR - compilation stops here
                        // The program never runs

With Python, the program runs until the problematic line is executed:

# Python - error caught at runtime
def add_numbers(a, b):
    return a + b

result = add_numbers(5, 10)           # Executes successfully: 15
result = add_numbers(5, "hello")      # RUNTIME ERROR: TypeError

With Python, the first call executes successfully. The error only appears when the program reaches the second call with incompatible types. The program runs without issue until that specific line is executed.

Safety Trade-offs

Statically typed languages are "safer" in one important sense: Type errors can never reach production because they are caught during compilation. This is why many mission-critical systems (banks, aerospace, medical devices) use statically typed languages like C, C++, or Java.

However, dynamically typed languages offer distinct advantages:

• Faster development: Code can be written more quickly without explicit type declarations
• Flexibility: Generic functions can be written that work with multiple data types
• Easier prototyping: Experimentation and iteration can proceed more rapidly

Dynamic typing represents a trade-off: developers gain flexibility and development speed, but sacrifice early error detection through compilation.

Python's Evolution: The Best of Both Worlds

In recent years, Python has evolved to include type hints (introduced in Python 3.5). Type hints allow developers to optionally annotate variable and function types, though Python remains dynamically typed at runtime:

# Modern Python with type hints (optional)
def add_numbers(a: int, b: int) -> int:
    return a + b

result = add_numbers(5, 10)           # Works: 15
result = add_numbers(5, "hello")      # Python executes this line without objection
                                       # but raises TypeError at runtime

Type hints are purely optional annotations; Python does not enforce them during execution. However, separate static analysis tools such as mypy can examine the code before runtime and detect type inconsistencies:

# mypy checks types before execution
$ mypy your_file.py
error: Argument 1 to "add_numbers" has incompatible type "str"; expected "int"

This approach provides the advantages of both paradigms: developers retain the flexibility and rapid development speed of dynamic typing, while gaining access to type checking before runtime through optional tooling.

Important Note: Design Choice, Not Necessity

Static versus dynamic typing is independent from compiled versus interpreted.

Python could theoretically be statically typed. For example, a statically typed version of Python could require developers to declare types upfront, and the interpreter could check those types during execution.
Similarly, C could theoretically be dynamically typed. A dynamically typed version of C could allow developers to create variables without declaring types, and the compiler could check the types of the values that variables hold during compilation instead of requiring upfront type declarations. For example, instead of requiring int age = 25;, a dynamically typed compiled language could allow age = 25; and automatically determine that the variable age currently holds an integer value.

The actual choices made by language creators reflect different priorities:

• Python's designers chose dynamic typing to prioritize ease of use and rapid development
• C's designers chose static typing to prioritize performance and early error detection

Each choice reflects different priorities and intended use cases.

Key Takeaways

• Python is dynamically typed: Types are checked during runtime, not at compile-time
• In dynamically typed languages type declarations are not required: although type hints can be optionally added
• In dynamically typed languages type errors appear during execution: when incompatible operations are attempted
• Static typing catches errors earlier: but requires explicit type declarations upfront
• Python's type hints with mypy provide type checking without sacrificing dynamic flexibility