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Linear Search in Python Program: All You Need to Know

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20th Sep, 2022
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Linear Search in Python Program: All You Need to Know


To search for an element in a given data structure, we implement certain algorithms to efficiently carry out our tasks. These kinds of algorithms are known as Searching Algorithms. Some commonly used Searching Algorithms are – Linear Search Algorithm, Binary Search Algorithm, Jump Search Algorithm, Interpolation Search Algorithm, etc.

Out of all these algorithms, Linear Search Algorithm is the oldest and the simplest searching algorithm. In this article, we will be learning how Linear Search Algorithm is implemented on a data structure and also be looking into the Python 3 implementation of the algorithm.


Linear Search Algorithm is the first searching algorithm starters often learn when they start learning data structures and algorithms. Let us take an example and see how Linear Search Algorithm can be implemented.

Let us consider we have the following data in a Python List and we want to search for the value = 30.


0                 1                 2                  3               4                     5                 6                7


  • Step 1: We consider the first value, i.e. 21 from the list, and then compare that value with our search value, i.e. 30. Since it is not equal, we move on to the next value in the list.
  • Step 2: Now, we consider the second element, i.e. 65, and compare it with value = 30. Since it is not equal, we again move on to the next element.
  • Step 3: We have now 7 on our list. On comparing the two elements, we find 7 is not equal to 30. Hence, we move to the next element.
  • Step 4: We have now element 30 on our list. We compare 30 with value = 30. Since 30 is equal to 30, we have found our desired element in the list. We can then print a message saying the element has been found and then return the index value = 3 of that element for further calculations.

As you can see, this is an iterative procedure, so this iteration will continue as long as the desired element is found or it reaches the end of the list. If it reaches the end of the list, but there is no such element found in the list, it can display a message saying, no such element is present.

So, this is the Linear Search Algorithm, and surely you must have found it out to be very simple.

Time Complexity

  1. Worst Case – O(n) where n is the number of elements in a list. For the worst case, the element is at the end of the list, the loop needs to be executed for n times.
  2. Best Case – O(1). For the best case, the element is found in the first position in the list.
  3. Average Case – O(n). Summation of all the time taken for each element in the list and getting the average of all the elements in the list gives us O(n) time complexity.

Python 3 Implementation

Now let us look at how we can implement this algorithm in Python.

Now when we give input as 30, let us see the output:

If we enter a value that is not present in the list, for example, value = 980, let us see what the output is:

That is indeed the correct output as there is no 980 element present in the list.

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Improving Linear Search in Python

Linear search in Python is also known as a sequential search. Up until a match is discovered or the entire list has been searched, every component of the list is successively checked. It has been noted that when looking for a key element, it is possible to keep looking for the exact key element.

The intention is for the procedure to be quicker if a similar element is searched repeatedly. As a result, in this situation, Linear Search may be enhanced by utilizing the following two techniques:

1. Transposition

The search process additionally optimizes and keeps advancing the element to the beginning of the array where the searching time complexity would be constant time. If the key element is discovered, it is swapped to the element an index earlier to maximize the number of searches for a specific key.

2. Move to Front 

The recently searched item is brought to the top of the list using the Move to Front Method. This linear search program in python is, therefore, fairly simple to use, but it also brings less often searched things to the front. This approach has a significant drawback in that it affects access time by pushing things that aren’t frequently searched to the front.

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Advantages of Linear Search Program in Python

  • The Python program for linear search is a technique which best used when a key component matches the initial element of the array since its execution time is 0 (n), where ‘n’ is the number of elements that comprise the array.
  • It is not necessary to arrange the list. A structured list is not necessary for linear searching, in contrast to a binary search.
  • It is not affected by additions or removals. Since the list doesn’t need to be categorized for the linear search, more members can be added and removed. Algorithms may need to reorganize the list after additions or removals, much like with other types of searching. It may occasionally suggest that a linear search might be more successful.
  • A advantage of a python program for linear search over a binary search is that a binary search requires that the data be in sorted order in contrast to a linear search. 

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Disadvantages of Linear Search

  • One of the major disadvantages of a linear search is that it takes a long time for really large arrays. Most of the time, linear searches can take up to days and they can prove to be quite inefficient in certain cases. 
  • Conversely, lengthy list searches move slowly whenever an essential element doesn’t match any element or when a crucial element matches the final element in the array. This is the worst scenario in the linear search method. Several times, people use linear searches on a list that has more than 100 elements, which makes it impossible to search for the element in the last position quickly. 

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In this article, we have looked into what is a Linear Search Algorithm and also studied in detail how to search for a certain element from a list using the Linear Search Algorithm. Lastly, we also saw how we could practically implement the Linear Search Algorithm using Python 3 as a language and get our desired output.

If you are interested in Data Science, you must check out IIIT-B and upGrad’s Online Data Science Courses which has been carefully crafted for working professionals and offers numerous case studies, hands-on workshops, 1-on-1 mentorship, and much more.


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1What are the space and time complexities of Linear Search?

Linear Search is an efficient approach when the data is not sorted but can be a little expensive in terms of time. The time and space complexities depend upon how much time the compiler takes to execute the program and how much memory the program consumes respectively. Below are the time and space complexities of the linear search.

2Differentiate between linear search and binary search?

The prominent differences between the linear search and binary search are as follows:
Linear Search:
1. The average time complexity of the linear search is O(n).
2. It is best suited for unsorted data.
3. Performs an iterative approach.
4. The key is present at the start of the list in the best case.
Binary Search:
1. The average time complexity of the binary search is O(log(n)).
2. It is best suited for sorted data.
3. Performs the divide and conquer approach.
4. The key is present at the middle of the list in the best case.

3What is the interpolation search?

Interpolation search is the optimized version of binary search where the average time to find the key element is O(log(log n)). In binary search, we always look up to the middle of the array. But in this method, the search can go to different locations depending on where the key is located. For instance, if the key is located near the last element of the array, the search will start from the end of the array.

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