In this modern-day, it would be short-sighted to overlook the importance of programming. It makes a lot of things easier to visualize and to implement changes that are important to humankind. Be it the implementation of Artificial Intelligence or Robotics; every aspect is implemented using a programming language. Moreover, programming languages modify from time to time, implementing modifications that make the usage of languages easier. One such implementation that has made usage of programming languages easier is Arrays. Know more about Python Array: A Step-By-Step Guide.
Arrays are a collection of similar types of items stored in contiguous memory locations. All the elements stored in the Arrays belong to a similar data type. It makes the manipulation of data easier giving access to multiple aspects of the area in a single flow. Every array is identified by a dedicated index value. The user can access the index value according to the elements needed.
Python allows the use of arrays using an array module. A user can also implement lists as arrays and manipulate the elements. Let us understand more about arrays in Python and the different in-built operations of Python.
What are Python Arrays?
Python Arrays are similar to any other traditional arrays. Python has inbuilt arrays that are similar to C arrays, ensuring the manipulation of similar data types. The arrays in Python are of two types, firstly NumPy initiated arrays and Inbuilt arrays.
Arrays of Python are used to store similar data types. They are data structures identical to lists used to store several values for data manipulation inside programming. Being one of the important aspects of the programming language, Python has implemented several in-built functions to access the elements of the area and manipulate the elements stored in the array. A programmer can perform several operations on the array, which include:
- Traversing the array
- Indexing
- Slicing
- Sorting
- Adding and removing elements
- And many more.
Python uses the simplest initiation of the array compared to any other programming language. The operations performed on the array also include several functions. Let us understand the common functions in the subsequent sections.
Declaring Arrays in Python
Python uses three methods to initialize an array in a program. Each method can be used in the program according to the requirement of the user and the program. Let us understand all three ways to declare Arrays in detail.
- Direct declaration
The simplest way to initialize an array in Python is by using the direct method. It includes an array name and the direct values that need to be inserted inside the array. The elements of the array are automatically induced, according to the indexes placed in the initialization.
Syntax:
Array_name = [default_value]
Or
Array_name = [default_value] * size
Example:
Cars = [BMW, Hyundai, Audi]
print(cars)
Or
Arr1 = [2] * 5
print(Arr1)
Output:
[BMW, Hyundai, Audi]
[2,2,2,2,2]
In the above example, users can set a default value of any data type inside the array. The syntax allows the addition of predetermined values to the list of the arrays without the need to iterate through all the indices of the array. It saves a lot of time in programming and also allows for easier manipulation of the data.
- Looping the elements to a particular index
If a program requires the declaration of an array to a particular index value, it can use the loop and range functions of the array. The loop and range function of Python helps the programmers to set the size of the area according to the requirement of the program.
Syntax:
[value for element in range(num)]
Example:
arr1 = [0 for in range (7)]
Output:
[0,0,0,0,0,0,0]
In the above example, the range takes the number of index values that the array is initiated for. The initialization creates an array starting with the index value 0 to 6; all the index values are incremented by 1.
The “for” is used as a looping statement that takes care of the element of the array. In the above example, all the elements in the arr1 are 0 by default.
- NumPy declaration
The NumPy module is also used in Python to create arrays and manipulate values. NumPy module uses a special function that helps to create an array in Python called numpy.empty(). The function allows the users to create an empty array of a specified size with a default value as None.
Syntax:
numpy.empty(size, dtype=object)
Example:
Import numpy as np
arr2= np.empty(13, dtype = object )
print (arr2)
Output:
[None None None None None None None None None None None None None ]
In the above example, 13 indicates the size of the array, and dtype indicates the type of data that needs to be stored in the array.
Note, all the elements of the array need to be of a similar data type. Python arrays do not require an initial declaration of the data type; the module identifies the data type as it is entered into the initiated array.
Accessing Elements of an Array in Python
Now that you have declared the elements of the array, using different methods, how can you retrieve the elements in the array in the further sections of the program? The data can be accessed in a multitude of ways. The methods used to access the elements are:
- Directly using the print statement.
- Using index values
- Looping
- Directly using the print statement
The most direct method to access or view the elements of the array is to use the print
statement. The print statement will allow the users to print the entire content of the array
in a single statement. The statement can be used to view the array, and cannot be used
to make modifications or use in the iterations. The print statement is used to view the
array.
Command:
Cars = [‘BMW’, ‘Audi’]
print(Cars)
Output:
Cars = [‘BMW’, ‘Audi’]
- Using index values
Elements in the array can be accessed by using the index values. Though you might not know the entire context of the array, using index values to retrieve the data can be used as an effective way to get the elements. To access the data using the index value, one can use the following command.
Command:
Cars = [‘BMW’, ‘Audi’]
x= Cars[0]
print(x)
Output:
[‘BMW’]
Here, cars[0] allow the users to access the index value in the array and retrieve the data placed there. The data can be stored inside a new variable and can be used for further program manipulation.
- Looping
Another method to access all the elements of the array adhering to the index value is by using the looping statement. The loop allows the users to retrieve the data in a constant loop using the for statement. The statement reads all the data mentioned in the array and prints it in dedicated format.
Command:
For x in Cars:
print(x)
Output:
‘BMW’
‘Audi’
The for loops allow the users to iterate throughout the array in a single go and print the elements necessary. Unlike the print statement, the elements that are retrieved from the loop can be stored in a fixed variable and used for further instructions of the program.
Array Representation and Formats
Python allows its users to initialize the array in several formats. A few formats that are used are:
- 2D arrays
- 3D arrays
2D arrays:
2D arrays involve a matrix representation of the array; this consists of several rows and columns that can contain different elements in the array. The items in this array can be accessed using dedicated row and column indices.
Syntax:
Array_name = [[rows] [Column]]
Example:
Arr2 = [ [1,0,0] [0,0,1] ]
Output:
[ 1, 0, 0 ]
[ 0, 0, 1 ]
3D arrays:
3D arrays are multidimensional arrays that contain several 2D arrays inside them. 3D arrays consist of block size, row size, and column size. The 3D array can store more data than 1D and 2D arrays.
Syntax:
Array_name = [ [ ‘element’ for col in range(num)] for col in range(num) ] for row in range(num) ]
Example:
Cars = [[‘None’ for col in range(5)] for col in range(5) ] for row in range(2) ]
print (Cars)
Output:
[ [ [ ‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ] ] ,
[ [‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ],
[‘None’, ‘None’, ‘None’, ‘None’, ‘None’, ] ] ]
Operations on Array
- Slicing
Slicing an array refers to accessing certain elements of an array. To put it in simple words, slicing acts by retrieving specific data. The method allows the users to access the data from any required position. The positions can include the center or the end of the array. Slices of the area allow the user to essentially break down the array as per the requirements of the program.
Syntax:
Variable_name = array_name[start index : end index]
You can skip either one of the initializations in the square brackets.
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
x= Cars [1:3]
y= Cars [: 3]
z= Cars [2:3]
print(x)
print(y)
print(z)
Output:
[ “Volvo”, “BMW”, “Hyundai” ]
[ “Audi”, “Volvo”, “BMW”, “Hyundai” ]
[“BMW”, “Hyundai”]
- Modification
Modification or updating of an array refers to changing the value present in the array with new information. Using the modification, the user can change the value of the element in a particular index and replace it with new information. The operation does not delete the item; it replaces it with a new item of the user’s choice.
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
Cars[4] = “Beat”
print (Cars)
Output:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Beat” ]
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
Cars[3] = “None”
print(Cars)
Output:
Cars = [ “Audi”, “Volvo”, “BMW”, “None”, “Verna” ]
Updating is performed using the index values of the array. One thing to keep in mind is that the array starts with a 0 index, and the place value or the index will vary accordingly.
- Append and Pop items of an array.
Arrays can be subject to modification in the course of the program. The modification or addition of a new object in the array can be carried out using the append function. On the contrary, if an element in the array needs to be removed, we can use the pop function. The use of both functions is mentioned in the examples.
Syntax:
Array_name.append()
Array_name.pop()
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
Cars.append(“Beat”)
print(Cars)
Output:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna”, “Beat”]
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
Cars.pop(3)
print(Cars)
Output:
Cars = [ “Audi”, “Volvo”, “BMW”, “Verna” ]
- Searching for elements in the array.
The search operation in Python allows the users to look for items on the array. The search operation uses the inbuilt function index to verify the presence of an element throughout the array. The index function works as a loop that iterates over the array and searches for the desired element.
Syntax:
Array_name.index(element)
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
x= Cars.index(“Volvo”)
print(“The index value is”, x)
Output:
The index value is 1
When using the index function to look for items in the array, it returns the index value of the item in the output. The index value can be further utilized in the program to implement the necessary instructions or array operations.
- Get the size of the array.
Arrays can be of validated size. The program sometimes demands the need to understand the area length to implement instructions. It uses the inbuilt len function of Python.
Syntax:
Variable = len(array_name)
Example:
Cars = [ “Audi”, “Volvo”, “BMW”, “Hyundai”, “Verna” ]
x= len(Cars)
print(“The length of the array is:” , x)
Output:
The length of the array is: 5
- Counting number of occurrence
Several applications of arrays require the need to sort the data and look for iterative data that can corrupt the memory. In some applications, arrays store a multitude of data for the same information; it is accessed by using the sum operation.
The sum operation can be implemented on NumPy arrays; this function counts the number of repetitions of the data the user is looking for and returns the exact number of replications found throughout the array.
Syntax:
Variable_name= (array_name == element). sum()
Example:
Num = np.array [1,2,3,4,3,1,5,6,1,4,6,8,4, 2]
Count = (Cars == 4). sum ()
print(“The occurrence of 4 is: ”, Count)
Output:
The occurrence of 4 is: 3
- Array to list
Array to list function converts the entire array to list.
Syntax:
Variable_name = array_name.tolist()
Example:
Import numpy as np
Num = np.array[ 1,2,3,4 ]
List_new = Num.tolist()
to list“\nList: ”)
print(List_new)
Other operations of array include:
- clear()
Used to eliminate all elements from the array list
- copy()
To return a copy of the array list
- count()
To return the elements along with their total number
- extend()
To add the elements of an array to the end of the current list
- And many more
When to use a Python Array?
Arrays are used in applications where a multitude of similar data is used for programming. It includes details of the company, students, products, and many more. Python arrays allow the users to dynamically modulate the data, thus providing faster and much greater access to space and time. Arrays can also be effectively put to use when the memory constraints on the program are high and the dedicated memory space is low.
Importance of Python Array
- Arrays allow the manipulation of similar data types in a single place. It reduces the variable size and global variables used by the program. The programmer does not need to monitor different variable values. Multiple variables often cause a lack of versatility in the programming language.
- It saves a lot of memory and time when implementing a program. As the time constraints are reduced, access to the array becomes quicker. The memory allocation is reduced to a significant level. It allows the users to reduce the redundant variable space giving easy access to all the elements without the clutter.
Conclusion
Arrays are a data structure that supports the storage of the same data type. Multiple programming languages use arrays to handle data.
Arrays are quite essential for any programming language. They help in manipulating data in a single space adding multiple benefits to the programming language. Arrays in Python are also extremely essential; they save the memory space as well as the time required to manipulate the data. Arrays being data structures that can store magnitudes of data of the same form in a single variable, the access of the elements becomes easier as well as efficient for the programmers, reducing the data clutter.
Arrays in Python also have several operations that can be performed to easily manipulate and update the area values. This includes append, pop, index, and many other functions. To get a hold of all the necessary details of Python arrays, go through the guide again and know more.
