Python Dictionaries

Dictionaries are used to store data values in key:value pairs. A dictionary is a collection which is ordered*, changeable and do not allow duplicates. As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered. Dictionaries are written with curly brackets, and have keys and values:

Dictionary items are ordered, changeable, and do not allow duplicates. Dictionary items are presented in key:value pairs, and can be referred to by using the key name.

Print the "brand" value of the dictionary:

As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered. When we say that dictionaries are ordered, it means that the items have a defined order, and that order will not change. Unordered means that the items do not have a defined order, you cannot refer to an item by using an index.

Dictionaries are changeable, meaning that we can change, add or remove items after the dictionary has been created.

Dictionaries cannot have two items with the same key:

Duplicate values will overwrite existing values:

To determine how many items a dictionary has, use the len() function:

Print the number of items in the dictionary:

The values in dictionary items can be of any data type:

String, int, boolean, and list data types:

Print the data type of a dictionary:

It is also possible to use the dict() constructor to make a dictionary.

Using the dict() method to make a dictionary: thisdict =

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.