Why Python is called Dynamically Typed?

Python is often referred to as a dynamically typed language, which contrasts with statically typed languages such as C, C++ and Java. Understanding the reasons behind this classification requires a closer look at how variables are handled in Python compared to other programming languages.

In a dynamically typed language, you don’t need to specify a variable’s type when you create it, the type is determined automatically based on the value assigned. In contrast, in statically typed languages, you must declare the type at compile time and it remains fixed throughout the variable’s life. Example:

x = 10  # integer
print(type(x))

x = "Hello"  # string
print(type(x))

x = [1, 2, 3]  # list
print(type(x))

<class 'int'>
<class 'str'>
<class 'list'>

Explanation: This code demonstrates Python’s dynamic typing x = 10 assigns an integer, x = “Hello” changes it to a string and x = [1, 2, 3] updates it to a list, with the type inferred based on the assigned value.

How Python’s dynamic typing works?

1. No type declarations: In languages like C, Java and C++, variables must be declared with a specific type before use. This allows the compiler to allocate the correct memory and interpret the values properly. For example in C, you declare an integer as:

int x = 10;

Here, x is explicitly declared as an integer, so the compiler knows it will always store an integer and allocates memory accordingly.

But in Python, the declaration step is skipped. You simply write:

x = 10

Python automatically determines that x is an integer based on the value assigned to it.

2. Type Assignment at Runtime: In Python, a variable’s type is determined at runtime based on the value it holds. When you assign a new value, Python automatically updates the variable’s type. For example:

x = 42 # integer

x = “Hello” # string

x = [1, 2, 3] # list

Here, x changes type based on the value assigned. Python figures out the type on its own, which is the essence of dynamic typing.

3. Memory Management: In statically typed languages, memory is allocated based on the declared type, and the compiler optimizes it—for example, an int in C typically takes 4 bytes.

In Python, memory is managed automatically using reference counting and garbage collection. When you assign a value to a variable, Python allocates memory dynamically for that specific value. For example:

x = 10

Python creates an integer object and links it to x. If you later assign a string to x, Python automatically updates the reference to the new string and handles the old value in the background.

4. Type Safety at Runtime: Even though Python is dynamically typed, it still checks types at runtime. For example:

x = 10

y = “Hello”

print(x + y) # Raises TypeError

Here, Python throws a TypeError because you can’t add an integer and a string. Unlike statically typed languages, which catch such errors at compile time, Python catches them while the program is running.

Advantages and Disadvantages of dynamic typing in Python

Let’s understand some of the key advantages and disadvantages of dynamic typing in Python.

himank bhalla

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• Python Programs

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