The Differences and Use Cases of Python Array and List
| Parameter | List | Array |
| Declaration | Lists need not be declared since they are inbuilt in Python
List = [10,20,30] |
Need to import an array module or NumPy library in order to declare an array
my_arr_1 = array.array(‘i’, |
| Data Type | A single list can contain values that belong to different data types
myList = [40, ‘hi there’, ‘m’] |
All elements of an array should be of the same data type
myArr = arr.array(i, [1,0,9]) |
| Size | Python list is resizeable as during list initialization, Python initializes some extra elements | Arrays have a constant size that can not be altered |
| Space/Memory | Consumes larger space and memory for the addition or removal of elements | Stores data in a more compact manner |
| Data Storage | Preferred for storing a small amount of data | Preferred for storing a large amount of data |
| Mathematical Operations | Can not be used for mathematical operations directly | Array elements can be easily manipulated using advanced mathematical operations |
| Display Data | Elements of a list can be displayed without loop
my_List = [1,”Dennis”,[‘a’,’b’]] print(my_List)
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A loop must be required for the elements of an array to be displayed.
Import array my_Arr = array.array(‘i’, [1,2,3]) for i in my_Arr: print(I) |
When we need to keep a tiny series of elements and don’t want to conduct any mathematical operations, the list data structure is the best option since it allows us to store an ordered, changeable (easily modified), and indexed sequence of objects without having to load any explicit modules. Because an array data structure allows for more efficient data storage of elements, it is recommended that we utilize one when dealing with a lengthy series of data. If want to execute numerical operations on a group of components, you should use an array, which is a data structure that largely depends on data analytics and data science. A python list is also quicker than a python array since a python array is based on a python list because when we build a python list, an array of pointers containing the references of items in the list is produced somewhere in memory.