Accessing All Elements at Given List of Indexes in Python

Python comes up with a straightforward solution for accessing elements from a list in Python using a list of indexes with an effortless procedure. A list's elements can be accessed by using the index number and the [] brackets. However, we cannot use this method when accessing some indices. In Python, various methods exist to obtain the corresponding elements from another list when you possess a list of indices. Now, let us explore a few approaches.

Access Elements Using List Comprehension

Going through the list comprehension method, you can effortlessly access the elements from the chosen index in a list. Let's see the code example below to understand this method better.

Example:

Output:

[10, 20, 30]

Explanation:

In the above-given example, values [5, 10, 15, 20, 25, 30, 35] are used in the original list, and we have accessed the [1, 3, 5] indexed elements. To get the result elements, we created a variable list named selected_elements. In the last used print statement to print the result.

Alternatively, we can use a while loop also to achieve the same result.

Code:

Output:

Element at index 0: 10
Element at index 1: 20
Element at index 2: 30
Element at index 3: 40
Element at index 4: 50

Explanation:

In the above code, i iterates through each value in the created variable named my_list through the while loop. The while loop initializes a counter variable i to 0 and iterates until i reaches the length of the list my_list. After each iteration, it will print the value at the current index i with element's index number. After printing, the counter i will be incremented by 1 to move to the next element in the list. The while loop stops once i will be equal to the length of the list, ensuring that all elements of the list are iterated through and printed with their corresponding indices.

Access Index of a List Using Loops

Here, we will access the chosen elements with the help of loops, including while and for loops. This technique enables further data manipulation and increases code maintainability.

Example:

Output:

[10, 20, 30]

Explanation:

After creating a list, we need to store the resulting elements to store the accessed elements from the list at the specified locations; that's why we created an empty list named chosen_elements. Next, use a for loop to repeat through the list of positions and add the compatible items to the chosen list as we proceed.

Accessing all Elements at the Given List Using NumPy

If you are working with arrays, NumPy enables you to retrieve elements from particular indexes effectively. Let's have a glance at the piece of code below.

Example:

Output:

[10, 20, 30]

Explanation:

In the provided code we created the array named array containing the values [5, 10, 15, 20, 25, 30, 35]. Then defined a list named indexes containing the indices [1, 3, 5]. By using the NumPy array indexing method, the code chooses elements from the array based on the indices specified in the indexes list and will collect them in a newly created array named chosen_values.

Accessing Multiple Elements Using map() and __getitem__

By utilizing the map() and __getitem__ functions, one can havethe ability to effortlessly retrieve the elements located at the specified list of indices. This method allows for seamless mapping of a list, retrieval of index elements, and comparison with elements from the search list.

Example:

Output:

Main list : [8, 4, 6, 9, 12, 16]
Main index list : [1, 3, 4]
Resultant list : <map object at 0x000001AEDFF169B0>

Explanation:

In the above code we have created two variable lists named tst_list containing values [8, 4, 6, 9, 12, 16] and ind_list containing indices [1, 3, 4]. Then it prints both lists for reference. Through the map() function, it accesses elements from tst_list provided in ind_list using the __getitem__ method, which outputs the elements by index. The map() function returns an iterator, and the res_list variable holds this iterator.

Using operator.itemgetter()

The operator.itemgetter() method is popular for obtaining the item at the given set of indices. This is the most straightforward approach to complete the task. Also, This function merges the elements of the original list with the required index. Below is an example of Python code.

Example:

Output:

Main list : [8, 4, 6, 9, 12, 16]
Main index list : [1, 3, 4]
Resultant list : [4, 9, 12]

• Time complexity: The above-provided code has an O(k) time complexity, where k is the length of the index_list.
• Space complexity: The itemgetter() function creates a new list along with k elements. Here, the space complexity of the program is O(k).

Explanation:

In the preceding code, the itemgetter() function was utilized from the operator module to retrieve the items from the test_list at the specified positions in the index_list. Subsequently, a list named test_list was generated having values [8, 4, 6, 9, 12, 16] and index_list containing positions [1, 3, 4]. By using the itemgetter(*index_list)(test_list) function, it retrieves items from test_list at the positions specified in index_list. The itemgetter() function fetches items based on positions and yields a tuple of the selected items.

Using numpy.take() function

The numpy.take() function effectively accesses the element from a provided index within a list. Before using the method, install the numpy library using "pip install numpy" in the command prompt, Jupyter Notebook, VS Code's terminal, or whatever you use. Let's see an example code to complete the task.

Example:

Output:

Main list : [8, 4, 6, 9, 12, 16]
Main index list : [1, 3, 4]
Resultant list : [4, 9, 12]

Explanation:

In the above code, the NumPy library is used to extract elements from the list tst_list from the indexes provided in the ind_list. After that, we imported NumPy as np, and created the list tst_list containing values [8, 4, 6, 9, 12, 16], and ind_list containing indices [1, 3, 4]. Using np.take(tst_list, ind_list) function, it is used the take() function from NumPy library to retrieve elements from tst_list from the provided indexes in ind_list.

Conclusion

Accessing specific elements based on given indices is a common task when working with Python lists. This article explored three methods: list comprehension, mapping with __getitem__, and utilizing the operator.itemgetter(). By employing these approaches, we can efficiently extract elements from a list, thereby facilitating the manipulation of specific indexes.