Introduction to Encapsulation
Encapsulation is a core principle of object-oriented programming that bundles data and methods while restricting direct access to internal details. It protects sensitive implementation, ensures stability, and provides safe, controlled interfaces for external use.
Encapsulation is more than data hiding. It is about balancing protection with usability. By exposing only what is necessary, classes stay flexible, stable, and capable of evolving without disrupting dependent code.
Encapsulation is guided more by conventions than by strict enforcement. Underscores signal intended access levels, but developers are trusted to respect them.
Encapsulation in Python is achieved through naming conventions:
-
Public members: no prefix, fully accessible, form the official API.
-
Protected members: prefixed with a single underscore (
_attribute), intended for internal use or subclass access. -
Private members: prefixed with double underscores (
__attribute), transformed through name mangling into identifiers like_ClassName__attribute, making them difficult to access externally.
Proper encapsulation improves security by protecting sensitive data, maintainability by allowing internal changes without breaking external code, and reliability through controlled state transitions.
A classic example is a BankAccount class, where attributes like balance and transaction history are private, and public methods manage deposits, withdrawals, and balance checks. These methods validate inputs, enforce rules, and log activity while keeping sensitive data hidden.
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Encapsulation is a core principle of object-oriented programming that bundles data and methods while restricting direct access to internal details. It protects sensitive implementation, ensures stability, and provides safe, controlled interfaces for external use.
Encapsulation is more than data hiding. It is about balancing protection with usability. By exposing only what is necessary, classes stay flexible, stable, and capable of evolving without disrupting dependent code.
Encapsulation is guided more by conventions than by strict enforcement. Underscores signal intended access levels, but developers are trusted to respect them.
Encapsulation in Python is achieved through naming conventions:
-
Public members: no prefix, fully accessible, form the official API.
-
Protected members: prefixed with a single underscore (
_attribute), intended for internal use or subclass access. -
Private members: prefixed with double underscores (
__attribute), transformed through name mangling into identifiers like_ClassName__attribute, making them difficult to access externally.
Proper encapsulation improves security by protecting sensitive data, maintainability by allowing internal changes without breaking external code, and reliability through controlled state transitions.
A classic example is a BankAccount class, where attributes like balance and transaction history are private, and public methods manage deposits, withdrawals, and balance checks. These methods validate inputs, enforce rules, and log activity while keeping sensitive data hidden.
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