Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy.

copy.deepcopy() creates a Deep Copy.

Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy.

copy.copy creates a Shallow Copy.

Multithreading usually implies that multiple threads are executed concurrently. The Python Global Interpreter Lock doesn’t allow more than one thread to hold the Python interpreter at that particular point of time. So multithreading in python is achieved through context switching. It is quite different from multiprocessing which actually opens up multiple processes across multiple threads.

Here you can also find a comprehensive guide on Python Django Tutorial that is very easy to understand.

Django is a web service used to build your web pages. Its architecture is as shown:

• Template: the front end of the web page 
• Model: the back end where the data is stored 
• View: It interacts with the model and template and maps it to the URL
• Django: serves the page to the user 

Numpy is written in C so that all its complexities are backed into a simple to use a module. Lists, on the other hand, are dynamically typed. Therefore, Python must check the data type of each element every time it uses it. This makes Numpy arrays much faster than lists.

Numpy has a lot of additional functionality that list doesn’t offer; for instance, a lot of things can be automated in Numpy.

Python has a private heap space that stores all the objects. The Python memory manager regulates various aspects of this heap, such as sharing, caching, segmentation, and allocation. The user has no control over the heap; only the Python interpreter has access.

Arguments are passed in python by a reference. This means that any changes made within a function are reflected in the original object.

Consider two sets of code shown below:

In the first example, we only assigned a value to one element of ‘l’, so the output is [3, 2, 3, 4].

In the second example, we have created a whole new object for ‘l’. But, the values [3, 2, 3, 4] doesn’t show up in the output as it is outside the definition of the function.

o generate random numbers in Python, you must first import the random module. 

The random() function generates a random float value between 0 & 1.

> random.random()

The randrange() function generates a random number within a given range.

Syntax: randrange(beginning, end, step)

Example – > random.randrange(1,10,2)

In Python, the / operator performs division and returns the quotient in the float.

For example: 5 / 2 returns 2.5

The // operator, on the other hand, returns the quotient in integer.

For example: 5 // 2 returns 2

The ‘is’ operator compares the id of the two objects. 

list1=[1,2,3]

list2=[1,2,3]

list3=list1

list1 == list2 🡪 True

list1 is list2 🡪 False

list1 is list3 🡪 True