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The Global Interpreter Lock in Python is a mechanism to synchronize the execution of threads. It allows only one thread to execute in the interpreter at a time, preventing multiple threads from executing Python bytecodes simultaneously.
Lists are mutable, meaning you can modify their elements after creation. Tuples, on the other hand, are immutable, and once defined, their elements cannot be changed. Lists are defined using square brackets, while tuples use parentheses.
The 'pass' statement in Python is a no-operation statement. It serves as a placeholder in situations where syntactically some code is required, but no action is desired or needed.
Decorators are a powerful and flexible way to modify or extend the behavior of functions or methods in Python. They are applied using the '@' symbol and are often used for aspects like logging, timing, or authentication.
Python uses a built-in garbage collector to manage memory. Objects that are no longer referenced are automatically identified and removed to free up memory. Developers do not need to explicitly allocate or deallocate memory.
In Python, 'self' is a convention for the first parameter of a class method. It refers to the instance of the class and is used to access variables and methods associated with that instance.
A shallow copy creates a new object, but does not create new objects for the elements within it. A deep copy, on the other hand, creates a new object and recursively adds copies of objects found in the original.
The 'with' statement is used to simplify resource management, such as file handling. It ensures that necessary clean-up code is executed, even if an error occurs, by using the context management protocol.
Python supports functional programming through features like first-class functions, higher-order functions, and support for anonymous functions using lambda expressions.
The Global Interpreter Lock (GIL) in Python is a mechanism that allows only one thread to execute in the interpreter at a time. This impacts multi-threading by preventing multiple threads from executing Python bytecodes simultaneously, making it challenging to achieve parallelism in certain scenarios.
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