Is Python better for certain programming needs?

2023-01-13 22:55:49 人气: 8
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In this article, we will discuss whether Is Python better for certain programming needs? like Competitive Coding. The answer is YES; python is better for coding. It makes code in fewer lines within a short period of time. Product-based companies require g...

In this article, we will discuss whether Is Python better for certain programming needs? like Competitive Coding.

The answer is YES; python is better for coding. It makes code in fewer lines within a short period of time.

Product-based companies require good coders, and one must pass the Competitive Coding round in order to advance to the interview rounds. Competitive coding is one such platform that will put your mental abilities as well as your speed to the test.

Speed is an area in which python is second to none. In contrast to conventional programming languages such as C, C++, and JAVA, the amount of code to be typed is drastically reduced. Another important point is that Python provides its users with a wide range of functionality, packages, and libraries that act as a supplement to the programmer's mental ability.

Finally, the best part about Python is that it is incredibly easy, and we don't h**e to waste time on unimportant things like input, output, and so on. It aids in refocusing our attention on the issue at hand.

Now we will see some of Python's features, which surely will attract you to try Python for Competitive Coding.

Variable Independence

Python does not require us to define variables and Data-Types before we use them. This also provides us range flexibility as long as it is within the hardware's sensible limitations, i.e. no need to worry about integer and ,long integer. Internally, type conversion is performed flawlessly.

NOTE

In Python nested loops, we can utilize the same variable name in both the inner and outer for-loop variables without the worry of inconsistent data or errors.

Common Functions like sorted, min, max, count, etc.

The min and max function assists us in determining the minimum and maximum element in a list respectively. The sorted function sorts a list, and the count function counts the number of occurrences of a specific element in a list.

The nicest part is that we can be confident that the Python libraries employ the best algorithms **ailable for each of the aforementioned tasks. For example, the sorted function is a special sorting algorithm called TIMSORT with a worst-case time complexity of O(n log n), which is the best that a sorting algorithm can offer.

Example

 
# input list inputList = [10, 3, 5, 5, 1, 4, 6, 20, 5] # printing maximum/greatest element in an input list print("Maximum/greatest element in an input list: ",max(inputList)) # printing minimum/smallest element in an input list print("minimum/smallest element in an input list: ",min(inputList)) # printing the sorted list print("Sorted list: ",sorted(inputList)) # printing the Number of occurrences/frequency of a list element print("The no. of occurrences of 5 is = ",inputList.count(5))

Output

On executing, the above program will generate the following output −

Maximum/greatest element in an input list: 20
minimum/smallest element in an input list: 1
Sorted list: [1, 3, 4, 5, 5, 5, 6, 10, 20]
The no. of occurrences of 5 is = 3

Python lists combine the best features of arrays and linked lists.

Python lists h**e the distinct capability of deleting specific elements while keeping memory locations contiguous. The concept of linked lists is rendered null and void by this feature. It's like a STEROIDS linked list! Besides this, Insertions can be performed in any location.

Example

 
# input list inputList = [10, 3, 5, 5, 1, 4, 6, 20, 5] # deleting element at specific index(here at 4th index) using del del inputList[4] print("After deleting the element at the 4th index", inputList) # deleting specific element(6) from list using remove() function inputList.remove(6) print("After deleting element 6 from the list:", inputList) # inserting at any arbitrary position inputList[-2] = "tutorialspoint" print("Inserting (tutorialspoint) at last second index:", inputList) # Getting sublists result = inputList[:2] print(result)

Output

On executing, the above program will generate the following output −

After deleting the element at the 4th index [10, 3, 5, 5, 4, 6, 20, 5]
After deleting element 6 from the list: [10, 3, 5, 5, 4, 20, 5]
Inserting (tutorialspoint) at last second index: [10, 3, 5, 5, 4, 'tutorialspoint', 5]
[10, 3]

Unique List Operations: Backtracking, Sub-Lists.

If we are unsure about the list size, we can get the last element by using the index position -1. Likewise, -2 can be used for the final element, and so on. As a result, we can go back in time. We also don't h**e to give a list size; thus it can be used as a dynamic allocation array.

As shown in the above example, a specified portion of a list can be extracted without tr**ersing the list. The fact that lists can hold multiple data types is surprising. Lists are no longer just a uniform collection of data components.

Functions can return multiple values

Functions in other programming languages typically return only one value, however in Python, we can return multiple values.

Example

 
# creating a function that returns multiple values def returnMultipleValues(*arr): el = arr[0] e2 = arr[1] return el,e2 x, y = returnMultipleValues(4, 6) print(x,' ',y) x, y = returnMultipleValues(1, 3, 5, 8, 1) print(x,' ',y)

Output

On executing, the above program will generate the following output −

4 6
1 3

A flexible number of arguments to a function

Arguments to a function can be passed in the form of a list, the size of which can change each time the function is called. In the preceding example, we invoked the function with two arguments, then with five.

If else and for loops are easy to use

Python's if-else statement allows us to search for a certain element in a list without h**ing to tr**erse the full list and verify each element.

Some programming languages h**e a for each loop idea that is slightly distinct from a for loop. It enables us to tr**erse a list by h**ing the loop variable take on the list values one at a time. Python includes each loop concept in the for loop itself.

Example

 
# input list inputList = [10, 3, 5, 5, 1, 4, 6, 20, 5] # searching elements using if-else statements which are made easy if 5 in inputList: print("True") else: print("False") # printing list elements using for each loop for e in inputList: print(e, end = ' ')

Output

On executing, the above program will generate the following output −

True

Code Indentation

In Python, code blocks are differentiated by their indentation. This improves code readability and helps build the habit of indenting our code in us.

Sets and Dictionaries Concepts

  • A Set is an unordered collection data type that can be iterated, mutated, and does not contain duplicate elements. It's similar to a list with no duplicate elements.

  • A dictionary is similar to a list in that its values can be retrieved using user-defined keys rather than conventional numeric index values.

Example

 
# input Set inputSet = {'t','u','t','o','r','i', 'a', 'l', 's'} # second 't' is dropped to **oid duplicates and the # set returns elements unorderly print("Set:", inputSet) # input dictionary inputDict = {'Cricketer': 'Dhoni', 'Country': 'India', 'Score': 100} # accessing values of a dictionary using keys print("Value of 'Cricketer': ", inputDict['Cricketer']) print("Value of 'Score': ", inputDict['Score'])

Output

On executing, the above program will generate the following output −

Set: {'r', 'l', 't', 'a', 'i', 'o', 's', 'u'}
Value of 'Cricketer':  Dhoni
Value of 'Score':  100

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