Compound Data Structure

In the last article, we talked about dictionaries and how they are composed of key-value pairs. However for a very simple key, we assigned a very simple value. What if I wanted to store more information about my groceries. What if instead of just storing price, I wanted to also store the country of origin. Let's explore how we can do this with compound data structures.

Recall the example from the previous article:

grocery_items = {'bananas': 1.29, 'apples': 2.99, 'papayas': 1.39

‍In order to add more information to each key in our dictionary, we can nest dictionaries within dictionaries.

grocery_items = {
                 'bananas': {'price': 1.29, 'country of origin': 'Guatemala'},
                 'apples': {'price': 2.99, 'country of origin': 'United
                  Kingdom'},
                 'papayas': {'price': 1.39, 'country of origin': 'Costa Rica'}
                }

I‍n this compound data structure, we have grocery_item keys like bananas, apples and papayas, and each of these keys has a dictionary composed of price and country of origin.

How to index a Compound Data Structure

Accessing compound data structures is similar to dictionaries (they are dictionaries at their core):

print(grocery_items['bananas'])

>> {'price': 1.29, 'country of origin': 'Guatemala'}

Now what if I just wanted to access the country of origin of the product?

‍You can also grab it based on it's key:

print(grocery_items['bananas']['country of origin'])

‍>> Guatemala

You just have to add an extra set of brackets to access a nested key. First we looked up the key bananas, then we went deeper and looked up country of origin which returned the value Guatemala