Tuples are used to store multiple items in a single variable.
A tuple is a collection which is ordered and unchangeable.
Tuples are written with round brackets.
Create a Tuple:
Tuple items are ordered, unchangeable, and allow duplicate values.
Tuple items are indexed, the first item has index
, the second item has index
When we say that tuples are ordered, it means that the items have a defined order, and that order will not change.
Tuples are unchangeable, meaning that we cannot change, add or remove items after the tuple has been created.
Since tuples are indexed, they can have items with the same value:
Tuples allow duplicate values:
To determine how many items a tuple has, use the
Print the number of items in the tuple:
Create Tuple With One Item
To create a tuple with only one item, you have to add a comma after the item, otherwise Python will not recognize it as a tuple.
One item tuple, remember the comma:
#NOT a tuple
thistuple = ("apple")
Tuple Items - Data Types
Tuple items can be of any data type:
String, int and boolean data types:
tuple2 = (1, 5, 7, 9, 3)
tuple3 = (True, False, False)
A tuple can contain different data types:
A tuple with strings, integers and boolean values:
From Python's perspective, tuples are defined as objects with the data type 'tuple':
What is the data type of a tuple?
The tuple() Constructor
It is also possible to use the tuple() constructor to make a tuple.
Using the tuple() method to make a tuple:
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.
*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.