Lists are used to store multiple items in a single variable.
Lists are created using square brackets:
Create a List:
List items are ordered, changeable, and allow duplicate values.
List items are indexed, the first item has index
the second item has index
When we say that lists are ordered, it means that the items have a defined order, and that order will not change.
If you add new items to a list, the new items will be placed at the end of the list.
Note: There are some list methods that will change the order, but in general: the order of the items will not change.
The list is changeable, meaning that we can change, add, and remove items in a list after it has been created.
Since lists are indexed, lists can have items with the same value:
Lists allow duplicate values:
To determine how many items a list has, use the
Print the number of items in the list:
List Items - Data Types
List items can be of any data type:
String, int and boolean data types:
list2 = [1, 5, 7, 9, 3]
list3 = [True, False, False]
A list can contain different data types:
A list with strings, integers and boolean values:
From Python's perspective, lists are defined as objects with the data type 'list':
What is the data type of a list?
The list() Constructor
It is also possible to use the list() constructor when creating a new list.
list() constructor to make a List:
Python Collections (Arrays)
There are four collection data types in the Python programming language:
- List is a collection which is ordered and changeable. Allows duplicate members.
- Tuple is a collection which is ordered and unchangeable. Allows duplicate members.
- Set is a collection which is unordered, unchangeable*, and unindexed. No duplicate members.
- Dictionary is a collection which is ordered** and changeable. No duplicate members.
*Set items are unchangeable, but you can remove and/or add items whenever you like.
**As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered.
When choosing a collection type, it is useful to understand the properties of that type. Choosing the right type for a particular data set could mean retention of meaning, and, it could mean an increase in efficiency or security.