Skip to content
Related Articles

Related Articles

Save Article
Improve Article
Save Article
Like Article

Why is python best suited for Competitive Coding?

  • Difficulty Level : Easy
  • Last Updated : 04 Oct, 2021

When it comes to Product Based Companies, they need good coders and one needs to clear the Competitive Coding round in order to reach the interview rounds. Competitive coding is one such platform that will test your mental ability and speed at the same time.

Who should read this?
    Any programmer who still hasn't tried python for
    Competitive Coding MUST give this article a read.
    This should clear up any doubts one has before 
    shifting to python.No matter how comfortable
    a programming language may seem to you right now
    Python is bound to feel even better.
    Python has a tendency of sticking to people
    like a bad habit !!

SPEED is a factor where python is second to none. The amount of code to be typed decreases drastically in comparison to conventional programming languages like C, C++, JAVA. Another most important point is that python arms its users with a wide variety of functionality, packages, and libraries that act as a supplement to the programmer’s mental ability. 
Ultimately the best thing about python is that it’s very simple and we need not waste much time on trivial matters like input, output, etc. It helps shift our focus to the problem at hand.
Here I’m gonna list out some of my favorite features of Python which I’m sure will encourage you to start trying python for Competitive Coding. 
 

1.Variable Independence 
Python doesn’t require us to declare variables and their Data-Types before using them. This also gives us the flexibility of range as long as it’s within reasonable limits of the Hardware i.e. no need to worry about integer and long integer. Type conversion is internally handled with flawless results. 

Amazing Fact !!
          For nested loops in python we can use the 
          same variable name in both inner and outer
          for-loop variables without fear of 
          inconsistent data or any errors !!

2.Common Functions like sorted, min, max, count, etc. 
The min/max function helps us to find the minimum/maximum element from a list. The Sorted function allows us to sort a list and the count function helps us to count the number of occurrences of a particular element in a list. 
The best thing is that we can rest assured that the python libraries use the best possible algorithms for each of the above operations. For example, the sorted function is a very special sorting algorithm called TIMSORT that has a worst-case time complexity of O(n log n) which is the best a sorting algorithm can offer. 



Reference: Python sorting algorithm

Python




# Python code to demonstrate working of min(),
# max(), sorted() and count()
arr = [10, 76, 87, 45, 22, 87, 90, 87, 66, 84, 87]
 
print("Maximum = ",max(arr))
print("Minimum = ",min(arr))
print("The sorted array is = ",sorted(arr))
print('Number of occurrences of 87 is = ',arr.count(87))

Output: 

('Maximum = ', 90)
('Minimum = ', 10)
('The sorted array is = ', [10, 22, 45, 66, 76, 84, 87, 87, 87, 87, 90])
('Number of occurrences of 87 is = ', 4)

3.Lists in python combine the best aspects of arrays and linked lists. 
Python lists provide the unique functionality of deleting specific elements while keeping the memory locations in a contiguous manner. This feature renders the concept of Linked lists null and void. It’s like a linked list on STEROIDS! Moreover, Insertions can be performed at any desired location.

Python




# Python code to demonstrate list operations
arr = [00, 11, 22, 33, 44, 55, 66, 77, 88, 99]
 
# deletion via index position
del arr[5]
print(arr)
 
# deletion via specifying particular element
arr.remove(22)
print(arr)
 
# insertion at any arbitrary position
arr[-1] = "A random number"
print(arr)
 
# concept of sub-lists
k = arr[:2]
print(k)

Output: 

[0, 11, 22, 33, 44, 66, 77, 88, 99]
[0, 11, 33, 44, 66, 77, 88, 99]
[0, 11, 33, 44, 66, 77, 88, 'A random number']
[0, 11]

4.Unique list operations – Backtracking, Sub-Lists. 
In case we are not sure about the list size then we can use the index position of -1 to access the last element. Similarly, -2 can be used for the second last element and so on. Thus we can backtrack a list. Also, we don’t have to specify any particular list size so it also works as a dynamic allocation array. 
A specific portion of a list can be extracted without having to traverse the list as is seen in the above example. A very astonishing fact about lists is that they can hold different data types. Gone are the days where lists used to be a homogeneous collection of data elements!!

  • Functions can return more than one value. 
    Typically functions in other programming languages can return only one value but in python, we can return more than one value!! as is seen in the following code snippet. 

Python




# Python code to demonstrate that a function
# can easily return multiple values.
def multi_return(*arr):
    k1 = arr[0]
    k2 = arr[1]
    return k1,k2
     
a,b = multi_return(11,22)
print(a,' ',b)
 
a,b = multi_return(55,66,77,88,99)
print(a,' ',b)

Output: 

11   22
55   66

5.A flexible number of arguments to a function. 
Arguments to a function may be passed in the form of a list whose size may vary every time we need to call the function. In the above example, we first called the function with 2 arguments and then with 5 arguments!! 

  • If else and for loops are much more User Friendly. 
    The if-else statement of python allows us to search for a particular element in a list without the need of traversing the entire list and checking each element. 
    Some programming languages have a concept of a for each loop which is slightly different from a for a loop. It allows us to traverse a list where the loop variable takes upon the list values one by one. Python incorporates each loop concept in the for loop itself.

Python




# Python code to demonstrate quick searching
 
arr = [1, 2, 3, 4, 5, 6, 7, 8, 9]
 
# searching made easy
if 3 in arr:
    print("YES")
else:
    print("NO")
 
#foreach loop
for i in arr:
    print(i,end = ' ')

Output: 

YES
1 2 3 4 5 6 7 8 9 
  • Code Indentation. 
    Python blocks of code are distinguished on the basis of their indentation. This provides better code readability and instills in us a good habit of indenting our code.
  • Concept of Sets and Dictionaries. 
    A Set is an unordered collection data type that is iterable, mutable and has no duplicate elements. It’s like a list that doesn’t allow duplicate elements. 
    A dictionary is like a list whose values can be accessed by user-defined keys instead of conventional numeric index values. 

Python




# Python code to demonstrate use of dictionaries
# and sets.
a = {'a','b','c','d','e','a'}
 
# the second 'a' is dropped to avoid repetition
print(a)
 
dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'}
print("dict['Name']: ", dict['Name'])
print("dict['Age']: ", dict['Age'])

Output: 

{'d', 'a', 'e', 'b', 'c'}
dict['Name']:  Zara
dict['Age']:  7
  • Robust input statements. 
    In competitive coding, we are often required to take ‘n’ space-separated integers as input and preferably save them in a list/array. Python provides functionality to do it all in a single line of code.!! 

Python3




# Python code to demonstrate how to take space
# separated inputs.
arr = [int(a) for a in input().strip().split(' ')]
 
print(arr)



My Personal Notes arrow_drop_up
Recommended Articles
Page :