Akshay
Data structures in Python programming are necessary as without data structures, you can’t add any data to your program. But, choosing the right data structure in Python programming is also essential as it can significantly affect the performance of your code.
Arrays in Python are a data structure commonly used for data storage in Python programs. This is because arrays in Python fit in most cases due to their unique functionalities. So, let’s understand the arrays in Python, their types and functionality in this blog.
What are arrays in Python?
Let us first begin by understanding exactly what arrays are. There are many data structures in Python programming and arrays are one of them.
You can use arrays in Python to hold a given amount of elements of the same type. Furthermore, there is an index or a key with which you can identify each element in an array. However, you must remember that each element in an array should be of the same data type as the keyword you used in the array.
Let’s understand it with an example. So, if you use the keyword “int”, all the array elements should be integers as int stands for integers.
What are the types of arrays in Python?
Python contains various arrays. Moreover, each array in Python has a specific use case and data structure. So, here are the main types of arrays that you can use in Python:
- One-Dimensional Arrays
One-dimensional arrays are the simplest types of arrays in Python programming. It is also known as a vector array. In a vector array, there is only a linear collection of elements. Moreover, each element has its own unique index for its identification. Furthermore, this type of array in Python finds use in representing sequences, lists, and ordered data collections.
- Multidimensional Array
With the help of a multidimensional array, we can stretch the concept of vector array to 2-D, 3-D and beyond. So, you can say that the arrays in multidimensional have more than just the length.
For example, a 2-D array contains both a length and a breadth. Hence, it can hold more elements than a vector array, which has the same length. Likewise, the 3-D arrays in Python consist of multiple 2-D arrays stacked on each other.
However, unlike vector arrays, multidimensional arrays can store more complex data. For example, images and even scientific data.
- Jagged arrays
A jagged array is popularly known as an array of arrays. It is because a jagged array is a more flexible form of an array. So, each sub-array can have a different length in it.
Unlike multidimensional arrays, you can use a jagged array to accommodate different lengths of sub-arrays. Hence, it finds use in dealing with irregular data like text data which has varying lengths.
- Dynamic Arrays (Lists)
Dynamic arrays are developed with the help of an algorithm. Hence, they are not exactly a type of array in Python. However, its function makes it very popular among users. You may want to use a dynamic array because it can grow itself as per your needs.
For example, a normal array always has a fixed size. So, it needs to be edited for further use. On the other hand, a dynamic array can grow itself. Hence, by doing so it eliminated the overflowing problem.
What are the benefits of using arrays in Python?
There are several benefits of using arrays in Python programming. Some of the benefits are as follows:
Efficient Memory management
Using arrays, you can streamline your data and make it more structured. It helps to use the memory to its optimum capacity efficiently.
Faster Element Access
Unlike many other data structures where an element can’t be accessed directly, the arrays in Python allow you to do so. Since arrays in Python index each element stored, you can access the element directly instead of going through all the elements.
Mathematical and Statistical Usage
The best use of an array can be mathematical and statistical computation. By using the modified arrays from NumPy, you can enhance the performance of your code for statistical computation and solving mathematical problems.
How to create an array in python?
In Python, an array can be created by importing an array from the array module. Below is an example of creating an array in Python.
Input
In this code, the array is imported from the built-in array module. Moreover, you can see how an element can be accessed from an array. To access an element from an array, you must mention the element’s position in the array.
For example, the position of the first element in the array is 0. Similarly, if you want to access the third element, Its position will be 2.
In addition, to modify an element, you can do it by mentioning the position of the element to be modified and then mentioning the modified element.
Output
How to insert an element in an array?
If you want to add an element in an existing array rather than replacing it, then you should use “the insert()” method. Here is an example of using the “insert()” method.
Input
In the above example, we have an array called my_list. We have first added an element “10” at position “2”. You can see it written as “my_list.insert(2, 10)”. The first number in method brackets, 2, is the position where the element has to be inserted, and the second number, 10, is the element to be inserted.
Moreover, to insert an element at the end of the array, we have used “insert(len(my_list), 30). But why? It is because if we don’t know the position of the last element in the array, it will still insert the element right at the end.
Output
Final Thoughts
Arrays in Python are crucial because their functions make it efficient to store data. You should keep exploring and practising the use of arrays in Python as it has much to offer within Python programming. You can also use the arrays from NumPy and other libraries to enhance the memory management.
