Python is one of the top programming languages used by programmers worldwide due to its easier application and efficiency. One other feature responsible for its popularity is its diverse user-friendly data structures. As digitization is taking over the world by storm, programming is playing a key role in managing several related applications. Instead of using time-consuming programming methods, Python offers a variety of data structures to simplify programming. These data structures are created with different features to accomplish different tasks, where users can opt for the one relevant to their programming. Amid various Python data structures, array and list are the most popular ones.
Array and list are the two most prominently used data structures with similar features. They are both used to store data and grant access to various containing elements for iteration and indexing. Data structures in Python can either be in-built or accessed through imported packages and modules. array and list data structures, being part of the Python programming languages, share many similarities for smooth implementation. So what makes these two different from each other?
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Let’s find out!
What is Array in Python?
An array is a homogenous collection of elements, placed contiguously in memory location so the system can have easier access and quick calculation. Homogenous collection means elements belonging to the same data type. An array can hold a fixed number of non-unique variables, placed in the ordered form under square brackets. Arrays can deal with large amounts of non-unique data as implementation is easier and element indices are run during computing. They also grant access to random containing elements.
An array holds multiple values under a single name but indexes different values for different elements in the memory. It is composed of two parts: an element, which is any stored item within an array, and an index, which is the numerical value assigned to each element, allowing easy modification to the user. Array sorts values in an ordered sequence to avoid using lengthy variables. An array module is used to implement arrays in Python.
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What is a List in Python?
The list is one of the four in-built data structures offered under Python. List and arrays share similarities, but list is comparatively more flexible, enabling different data elements in its list. Furthermore, the list provides users the ability of negative indexing.
Along with these features, list allows item duplication, mutability, concatenations, slicing, and nested lists. Items in list are represented by elements inside the square brackets, separated by quotes and commas. Each element holds its unique place; therefore, access through the index is extremely easy. Implementation is an easy task under list, but indexing is a hassle – users cannot delete or add elements from the beginning of the list. It is only possible at the end of the list.
List, just like array, features elements in an ordered fashion. While mutability is a valuable option in the data structure, list does not provide users with the freedom to rotate or add elements. Users may change the value of an item in the list, but it is not possible to rotate the elements or add one from the beginning of the list.
Difference Between Array and List in Python
Array and list data structures in Python share various similarities in operations, but their differences are more prominent. Features such as modification abilities, numeric functions, data type storage, replaceability, declaration, and memory usage are a few aspects where they widely differ:
|
Array |
List |
| Array needs to be declared using any array module or NumPy library. | List is in-built in Python and does not require declaration. |
| Contains similar types of non-unique data type elements only. | Supports all data type elements in the list. |
| Size cannot be changed once an array is established. | List size is resizable and can be changed. |
| Stores data in a more compact manner, also cannot be resized. | Requires extra space to resize and index additional elements. |
| Modifications like addition, deletion, or updating data elements are complex as it runs through every single element at a time. | Changes like addition, deletion, or updating data elements are easy. |
| They are preferred to store large amounts of data. | They are used to store small amounts of data. |
| A loop is necessary for array elements to be displayed. | Loop is not required to display elements of a List. |
These are some of the significant differences between these two data structures in Python. While comprehending the differences is easy, knowing which one to implement during programming can be confusing.
Array or List: Which One to Use?
Array and list have their own contrasting features relevant to Python operations. There is no better of these two. Implementation entirely depends on the element being stored and the operation it is meant to perform. The question of when to use these should be solved by comparing a few features.
If the programmer intends on performing functions on a small data set, then a list is the preferred data structure. On the other hand, array is the right choice to deal with longer data sequences if the retrieved data set is bigger.
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Array also performs excellent with numerical operations. The NumPy module supports an array by performing various mathematical operations, including logarithmic functions and trigonometry. Contrary to it, a list cannot perform any kind of mathematical operations. Therefore, a list must be used for data that does not need any arithmetic operations.
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Conclusion
Now that you have an idea of the basic features of these two, understanding which data structure would be the most efficient for your programming operation should not be a problem. Array and list both are efficient programming data structures with their own advantages and disadvantages, created to accomplish different tasks. Continuous practice and implementation will offer you the proficiency to make the best use of array and list according to the type of data stored and the operations performed.
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