Here are the advantages of Python over other languages.

• Presence of Third Party Modules
• Extensive Support Libraries
• Open Source and Community Development
• Learning Ease and Support Available
• User-friendly Data Structures
• Productivity and Speed

Steps in managing memory in Python are -

• The Python memory is primarily managed by Python private heap space. 
• All Python objects and data structures are located in a private heap. 
• The programmer does not have access to this private heap and interpreter takes care of this Python private heap. 
• The allocation of Python heap space for Python objects is done by Python memory manager. 
• The core API gives access to some tools for the programmer to code. 
• Python has an inbuilt garbage collector, that recycles all the unused memory and frees the memory and makes it available to the heap space.

Due to Python's dynamic nature. The answer is one of:

• the byte size of the name of a variable as a string, plus two bytes/pointers
• one byte/pointer plus one byte/pointer for every unique attribute
• the memory of your entire application >more than the current memory of your application
• something else, depending on your definition of a variable

The index for the negative number starts from ‘-1’ that represents the last index in the sequence and ‘-2’ as the penultimate index and the sequence carries forward like the positive number.

The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in the correct order.

We use python numpy array instead of a list because of the below three reasons:

• Less Memory
• Fast
• Convenient

Monkey patching is changing the behavior of a function or object after it has already been defined.

Most of the time it's a pretty terrible idea - it is usually best if things act in a well-defined way. One reason to monkey patch would be in testing. The mock package is very useful to this end.

• Only use lower case while naming a Package in Python.
• Python loves the small case alphabets and hence suggests Module names should also follow the suite.
• Classes in Python subject to a different naming scheme called as CapWords convention.
• Global variables must all be in lowercase.
• Every instance method should have ‘self’ as its first argument.
• All class methods should have ‘cls’ as their first argument.
• All constants must be CAPITALISED completely.

• Python maintains a count of the number of references to each object in memory. If a reference count goes to zero then the associated object is no longer live and the memory allocated to that object can be freed up for something else.
• Occasionally things called "reference cycles" happen. The garbage collector periodically looks for these and cleans them up. An example would be if you have two objects o1 and o2 such that o1.x == o2 and o2.x == o1. If o1 and o2 are not referenced by anything else then they shouldn't be live. But each of them has a reference count of 1.
• Certain heuristics are used to speed up garbage collection. For example, recently created objects are more likely to be dead. As objects are created, the garbage collector assigns them to generations. Each object gets one generation, and younger generations are dealt with first.

By using the escape character backslash.

Eg. String=”Hello I am a string\

         And I continue that string.”

• Division: 2.x returns integer, 3.x always gives float
• Print: 3.x always requires parenthesis
• Unicode: In Python 2, implicit str type is ASCII. But in Python 3.x implicit str type is Unicode

dict (zip(t1,t2)

here t1 and t2 are the two sequences. Note: both sequences should be of same length.

def pyfunc(r):

for x in range(r):

print(''*(r-x-1)+'*'*(2*x+1))

count sum(1 for line in fh for character in line if character.isupper())

• Flask provides simplicity, flexibility and fine-grained control. It is unopinionated (it lets you decide how you want to implement things).
• Django provides an all-inclusive experience: you get an admin panel, database interfaces, an ORM, and directory structure for your apps and projects out of the box.

map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions.

• In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera.
• All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors.
• Thus, NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms.
• If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

def BinarySearch(lys, val):  

    first = 0

    last = len(lys)-1

    index = -1

    while (first <= last) and (index == -1):

        mid = (first+last)//2

        if lys[mid] == val:

            index = mid

        else:

            if val<lys[mid]:

                last = mid -1

            else:

                first = mid +1

    return index

def InterpolationSearch(lys, val):  

    low = 0

    high = (len(lys) - 1)

    while low <= high and val >= lys[low] and val <= lys[high]:

        index = low + int(((float(high - low) / ( lys[high] - lys[low])) * ( val - lys[low])))

        if lys[index] == val:

            return index

        if lys[index] < val:

            low = index + 1;

        else:

            high = index - 1;

    return -1

JSON stands for JavaScript Object Notation.

JSON is generally built on the following two structures:

• A collection of <name,value> pairs
• An ordered list of values.

We use load() function from JSON module in python.

It is highly dependant on what we mean by image processing here but a good starting point will be PIL library. It can be used to easily import images, do basic operations like rotation and resizing and saving images.

This is because of a number of reason, the main points being:

• It has most of the libraries used for ML
• All the heavy backend is done in c or fortran hence speed issue does not come up
• It also has gained a historical consistency in this sector
• Python codes have  great readability and helps in concentrating more in logic than syntax
• Python codes are in general much shorter which means lesser development time

Julia is the fastest language, so much so that a code which takes 5 seconds in Julia takes more than half an hour in python or R.

R is mainly for data scientist and not much anticipated for general purpose programming, hence for programmers, learning R is a bit of limiting.

Python has a balance between usability and speed. Its versatility makes it the first choice of programmers.

According to all the current researches, the future ( at least the near future) is going to be Artificial Intelligence and Machine Learning. Python is a programming language getting popular day by day due to its reduced complexity and the freedom it provides to work in various sectors of programming.

And because of high usage of python in the above field, it is considered one of the best languages to design a project related to AI and Machine Learning (although there are other things too used for that). So, Python serves to be the best ingredient of the dessert.