How Does Python Achieve Both Strong and Dynamic Typing?

Python's Strong and Dynamic Typing

Static typing, commonly found in languages like C , defines the type of a variable upfront, restricting its behavior and the operations it can perform. By contrast, dynamic typing assigns types to values at runtime, offering flexibility but potentially leading to unexpected type changes.

Python leans towards strong typing by enforcing type consistency throughout its execution. Unlike weakly typed languages, a value cannot magically change its type without an explicit conversion. For instance, a numerical string will not automatically transform into a number.

Despite its strong typing, Python also introduces dynamic typing by assigning types to its runtime objects. This means that the variables themselves do not inherit specific types. Instead, they can reference values of different types, resulting in lines like "bob = 1" followed by "bob = 'bob'." In each case, the variable "bob" maintains its flexibility as it can refer to different objects.

In summary, Python's unique blend of strong and dynamic typing allows for strict type enforcement while retaining the flexibility to change the type of variables at runtime. This provides a balanced approach, offering both type safety and the capacity to accommodate changing object types as needed.

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