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Why is python best suited for concurrent coding?

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When it comes to companies based on products they need good programmers and have to go through the Competitive Coding step to progress through the interview rounds. Competitive coding — one such platform that will test your intelligence 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 habbit !! 

SPEED — this is a factor where Python is unmatched. The amount of typed code is drastically reduced compared to conventional programming languages ​​such as C, C++, JAVA. The other most important point is that python equips its users with a wide range of functionalities, packages, and libraries that act as an addition to the mental capacity of the programmer. 
Ultimately, the best thing about Python — it is that it is very simple and we don’t have to spend a lot of time on trivial issues like input, output, etc. It helps to shift our attention to the problem at hand.

Here I am going to list some of my favorite Python features that I’m sure will encourage you to start trying Python for concurrent coding.

  1. Variable Independence
    Python doesn’t require us to declare variables and their data types before using them. It also gives us the flexibility of the range as long as it is within reasonable hardware limits, i.e. no need to worry about integers and long integers. Type conversions are handled internally 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. General functions like sort, min, max, count, etc.
    The min / max function helps us find the minimum / the maximum item from the list. The Sorted function allows us to sort the list, and the count function helps us count the number of occurrences of a certain element in the list. 
    Best of all, we can be sure that the python libraries use the best possible algorithms for each of the above operations. For example, the sorted function — it is a very special sorting algorithm called TIMSORT, which has the worst time complexity O (n log n), which is the best sorting algorithm. 
    Link: Python sorting algorithm

    # Python code to demonstrate how min () works,
    # 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 ))


     (’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 related of lists.
    Python lists provide the unique functionality of removing specific elements while preserving memory space in a contiguous manner.  This feature invalidates the concept of linked lists . It’s like a linked list on steroids! In addition, inserts can be done anywhere.

    # Python code to demonstrate list operations

    arr = [ 00 , 11 , 22 , 33 , 44 , 55 , 66 , 77 , 88 , 99 ]

    # deleted index position

    del arr [ 5 ]

    print (arr)

    # deletion by specifying a specific element

    arr.remove ( 22 )

    print (arr)

    # insert at any arbitrary position

    arr [ - 1 ] = " A random number "

    print (arr)

    # concept of sublists

    k = arr [: 2 ]

    print (k)


     [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 of operations — Backtracking, Sub-Lists.
    In case we are not sure about the size of the list, we can use index position -1 to access the last item. Likewise, -2 can be used for the second last item, and so on. This way we can keep track of the list. Also we don’t need to specify any specific size of the list, so it also works like a dynamic allocation array. 
    A specific part of the list can be retrieved without having to traverse the list, as seen in the above example.  It is quite surprising that lists can contain different types of data. Gone are the days when lists were a homogeneous collection of data items !!
  5. Functions can return more than one value .
    Usually functions in other programming languages ​​can only return one value, but in Python we can return more than one value !! as you can see from the following code snippet.

    # 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)


     11 22 55 66 
  6. Flexible number of arguments for the function.
    Function arguments can be passed as a list, the size of which can change 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 !!
  7. If also for a loop is much more user-friendly.
  8. The python if-else statement allows us to search for a specific element in a list without having to traverse the entire list and check each item. 
    Some programming languages ​​have the concept of a foreach loop, which is slightly different from a for loop. This allows us to traverse a list where the loop variable takes the list values ​​one at a time. Python includes the concept of a foreach loop in the for loop itself.

    < / table>


     YES 1 2 3 4 5 6 7 8 9 

  9. Code indentation. 
    Blocks of Python code are distinguished by their indentation. This provides better code readability and instills in us a good habit of indenting our code.
  10. The concept of sets and dictionaries.
    Set — it is an unordered collection data type that is iterative, mutable, and has no duplicate elements. It’s like a list that doesn’t allow duplicate items. 
    A dictionary is like a list whose values ​​ can be accessed using custom keys instead of the usual numeric index values.

  11. # Python code to demonstrate quick search


    arr = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]

    # search made easier

    if 3 in arr:

      print ( "YES" )

    else :

    print ( "NO" )

    forefore loop

    for i in arr:

    print (i, end = ’’ )

    # Python code to demonstrate the use of dictionaries
    # and installs.

    a = { ’a’ , ’ b’ , ’c’ , ’d’ , ’ e’ , ’a’ }

    # the second a is discarded to avoid repetition 

    print (a)


    dict = { ’Name’ : ’ Zara’ , ’Age’ : 7 , ’Class’ : ’First’ }

    print ( "dict [’ Name’]: " , dict [ ’Name’ ])

    print ( "dict [’ Age’]: " , dict [ ’ Age’ ])


     {’d’,’ a’, ’e’,’ b’, ’c’} dict [’ Name’]: Zara dict [’Age’]: 7 
  12. Safe input operators.
    In concurrent coding, we often need to accept whitespace-separated integers as input, and preferably store them in a list / array. Python provides the functionality to do it all in one line of code. !!

    # Python code to demonstrate how to get in place
    # split inputs.

    arr = [ int (a) for a in input (). strip (). split ( ’’ )]


    print (arr)

  13. All the above mentioned features are just a taste of Python’s real power. Python gives us plenty of reasons to stick around and its not just in the field of competitive coding. Programmers from every department be it Web Development, Machine Learning, Data Science etc are mostly shifting to python. In today’s industry even Companies are shifting their code base to python and so its better to go with the flow and learn python as early as possible and explore its unending pool of cool features !!

    This Contributed by Siddhant Bajaj . If you are as Python.Engineering and would like to contribute, you can also write an article using or by posting an article contribute @ See my article appearing on the Python.Engineering homepage and help other geeks.

    Please post comments if you find anything wrong or if you’d like to share more information on the topic discussed above.


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