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Python Array Declaration

Python Array Declaration

Hey, readers. Hope you all are doing well. In this article, we would be primarily focusing on Variants of Python Array Declaration.


What is a Python Array?

As we all know, Python offers various data structures to manipulate and deal with the data values.

When it comes to ARRAY as a data structure, Python does not offer a direct way to create or work with arrays. Rather, it provides us with the below variants of Array:

  • Python Array Module: The Array module contains various methods to create and work with the values.
  • Python List: List can be considered as a dynamic array. Moreover, heterogeneous elements can be stored in Lists, unlike Arrays.
  • Python NumPy Array: NumPy arrays are best suitable for mathematical operations to be performed on a huge amount of data.

Having understood about Python Array, let us now understand the ways through which we can declare an array in Python.


Python Array Declaration – Variants of the Python Array

In the below section, we will understand the techniques through which we can declare an array using the variants of Python array.


Type 1: Python Array module

Python Array module contains array() function, using which we can create an array in the python environment.

Syntax:

array.array('format code',[data])
  • format_code: It represents the type of elements to be accepted by an array. The code ‘i’ represents numeric values.

Example:

import array
arr = array.array('i', [10,20,30,40,50])
print(arr)

Output:

array('i', [10, 20, 30, 40, 50])

Type 2: Python List as an Array

Python list can be used to dynamically create and store the elements like an array.

Syntax:

list = [data]

Example:

lst = [10,20,30,40, 'Python']
print(lst)

Output:

[10, 20, 30, 40, 'Python']

As mentioned above, elements of various data types can be stored together in List.


Type 3: Python NumPy array

NumPy module contains various functions to create and work with array as a data structure.

The numpy.array() function can be used to create single as well as multi-dimensional array in Python. It creates an array object as ‘ndarray’.

np.array([data])

Example: Array creation using numpy.array() function

import numpy
arr = numpy.array([10,20])
print(arr)

Output:

[10 20]

Further, we can use numpy.arange() function to create an array within the specific range of data values.

numpy.arange(start,stop,step)
  • start: The starting element of the array.
  • end: The last element of the array.
  • step: The number of interval or steps between array elements.

Example:

import numpy
arr = numpy.arange(1,10,2)
print(arr)

Output:

[1 3 5 7 9]

Conclusion

By this, we have come to the end of this topic. Feel free to comment below, in case you come across any question.


References