Lists and Tuples are both sequence data types that can store a collection of objects in Python. The objects stored in both sequences can have different data types. Lists are represented with square brackets ['sara', 6, 0.19], while tuples are represented with parantheses ('ansh', 5, 0.97).

But what is the real difference between the two? The key difference between the two is that while lists are mutable, tuples on the other hand are immutable objects. This means that lists can be modified, appended or sliced on-the-go but tuples remain constant and cannot be modified in any manner.

Global variables are public variables that are defined in the global scope. To use the variable in the global scope inside a function, we use the global keyword. Protected attributes are attributes defined with a underscore prefixed to their identifier eg. _sara. They can still be accessed and modified from outside the class they are defined in but a responsible developer should refrain from doing so. Private attributes are attributes with double underscore prefixed to their identifier eg. __ansh. They cannot be accessed or modified from the outside directly and will result in an AttributeError if such an attempt is made.

Pass by value: Copy of the actual object is passed. Changing the value of the copy of the object will not change the value of the original object.

Pass by reference: Reference to the actual object is passed. Changing the value of the new object will change the value of the original object.

In Python, arguments are passed by reference, i.e., reference to the actual object is passed.

Python private heap space takes place of memory management in Python. It contains all Python objects and data structures. The interpreter is responsible to take care of this private heap and the programmer does not have access to it. The Python memory manager is responsible for the allocation of Python heap space for Python objects. The programmer may access some tools for the code with the help of the core API. Python also provides an inbuilt garbage collector, which recycles all the unused memory and frees the memory and makes it available to heap space.

Upon exiting, Python’s built-in effective cleanup mechanism comes into play and try to deallocate or destroy every other object. However, Python modules that are having circular references to other objects or the objects that are referenced from the global namespaces aren’t always deallocated or destroyed. This is because it is not possible to deallocate those portions of the memory that are reserved by the C library.

In Python, some amount of coding is done at compile time, but most of the checking such as type, name, etc. are postponed until code execution. Consequently, if the Python code references a user-defined function that does not exist, the code will compile successfully. The Python code will fail only with an exception when the code execution path does not exist.

We should use the threading module to implement, control and destroy threads for parallel execution of the server code. Locks and Semaphores are available as synchronization objects to manage data between different threads.

Parameter 'self' is used to refer to the object properties of a class. 'self' parameter is supposed to be prefixed to the class object properties.

In simple words, abstraction can be defined as hiding of unnecessary data and showing or executing necessary data. In technical terms, abstraction can be defined as hiding internal process and showing only the functionality. In Python abstraction can be achieved using encapsulation.

Yes, this is valid. The function will then return a None object. The end of a function is defined by the block of code is executed (i.e., the indenting) not by any explicit keyword.

We use a shallow copy when a new instance type gets created. It keeps the values that are copied in the new instance. Just like it copies the values, the shallow copy also copies the reference pointers. Reference points copied in the shallow copy reference to the original objects. Any changes made in any member of the class affect the original copy of the same. Shallow copy enables faster execution of the program.

Deep copy is used for storing values that are already copied. Unlike shallow copy, it doesn’t copy the reference pointers to the objects. Deep copy makes the reference to an object in addition to storing the new object that is pointed by some other object. Changes made to the original copy will not affect any other copy that makes use of the referenced or stored object. Contrary to the shallow copy, deep copy makes execution of a program slower. This is due to the fact that it makes some copies for each object that is called.

Even though Python comes with a multi-threading package, if the motive behind multithreading is to speed the code then using the package is not the go-to option.

The package has something called the GIL or Global Interpreter Lock, which is a construct. It ensures that one and only one of the threads execute at any given time. A thread acquires the GIL and then do some work before passing it to the next thread.

This happens so fast that to a user it seems that threads are executing in parallel. Obviously, this is not the case as they are just taking turns while using the same CPU core. Moreover, GIL passing adds to the overall overhead to the execution.

Hence, if you intend to use the threading package for speeding up the execution, using the package is not recommended.

When we want a function with a single expression, we can define it anonymously. A lambda expression may take input and returns a value. To define the above function as a lambda expression, we type the following code in the interpreter:

Objects and data structures in Python lie on a private heap. The Python memory manager internally manages this. It delegates some work to object-specific allocators while ensuring they operate only within the private heap. Actually, the interpreter manages this heap; the user has no control over it- not even if they manipulate object pointers to memory blocks inside the heap. The Python memory manager allocates heap space to objects and other internal buffers on demand.

Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists. Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.