JSON

JavaScript Object Notation--a way of representing data (not just in JavaScript but across many languages)

JSON (prounounced “jay-sahn”) stands for “JavaScript Object Notation”. And, sure enough, JSON is a notation that can be used to represent objects in the JavaScript programming language.

But, it’s usage is much broader than that. It is actually a convenient format for exchanging data among many kinds of computing applications, especially web and mobile applications. It is used not only by software written in JavaScript, but also software written in Python, Ruby, Java, and many other languages.

Examples

Resource:

It’s probably best to start with some examples. Here is an example of some JSON data. This JSON data represents the nutriments of a single soft drink (for this case, Coca Cola Coke) taken from the “Open Food Facts” dataset from the “ProgrammableWeb” collection of datasets.

1
2
3
4
5

 {
      "Salt": "0.01016",
      "Sodium": "0.004"
      "Sugars": "10.6", 
 }

This JSON object has three key/value pairs. That makes it similar to a Python Dictionary. In fact, if this JSON is stored in an API on the web, we can read it into a Python dictionary with this code:

1
2
3
4
5
6
7
8
9

import json
import requests
from pprint import pprint

cola_json = requests.get(
              'https://world.openfoodfacts.org/api/v0/product/4890008100309.json')
cola_data = cola_json.json()

pprint(cola_data)

On line 5-6, we create a response object cola_json which stores the request-response from the url https://world.openfoodfacts.org/api/v0/product/4890008100309.json.

In order to retrieve the data from the response object, we need to convert the raw response content into a JSON type data structure. This is achieved by using .json() method on line 7. The .json() method returns a Python dictionary object (type dict) that represents that same data as the JSON that was returned from url.

The pprint(cola_data) line gives us a printout of the Python Dictionary representation of this JSON object. That representation is very large, so we don’t include here on this web page, but, you can see it at this link

As you check out the provided link, since cola_data is a Python dictionary, you may have noticed that cola_data is a nested dictionary—a dictionary within a dictionary. Notice that the JSON object we dealt with before appears inside of this one. (Note: Some parts of the data have been cut out and replaced with ... so that we can see the overall structure of the remaining data more clearly.)

{'code': '4890008100309',
 'product': {'_id': '4890008100309',
             ...
             'nutriments': {'carbohydrates': '10.6',
                            ...
                            'salt': '0.01016',
                            ...
                            'sodium': '0.004',
                            ...
                            'sugars': '10.6',
                            ...
                            'sugars_value': '10.6'},
             ...
             'update_key': 'nutriscore-20171111'},
 'status': 1,
 'status_verbose': 'product found'}

Let’s break this down a bit. This entire sequence is a single Python dictionary that has a sequence of keys and values. Recall that cola_data is a nested dictionary, so it has multiple “levels” of dictionaries—each level consisting its own sequence of keys and values.

At the highest (or first) level, let’s identify the keys in this dictionary. They are:

At the next level, let’s look at the value associated with the key 'product'. This value is shown below:

 'product': {'_id': '4890008100309',
             ...
             'nutriments': {'carbohydrates': '10.6',
                            ...
                            'salt': '0.01016',
                            ...
                            'sodium': '0.004',
                            ...
                            'sugars': '10.6',
                            ...
                            'sugars_value': '10.6'},
             ...
             'update_key': 'nutriscore-20171111'},

What we see above is that the key 'product' is associated with a value as a big dictionary, which means we can go further down the “level”. In this nested dictionary, we see that one of the keys 'nutriments' is associated with a value as another dictionary—that is, the JSON object we considered earlier.

'nutriments': {'carbohydrates': '10.6',
               ...
               'salt': '0.01016',
               ...
               'sodium': '0.004',
               ...
               'sugars': '10.6',
               ...
               'sugars_value': '10.6'},

Using the Python Dictionary Representation of the JSON Object to Answer Questions

Suppose that cola_data represents the Python Dictionary representation of the JSON associated with the product lookup from the sample Python code in the article above. A complete listing of that JSON appears at this link.

Write a Python expression that would answer each of these questions.

  1. Q: What is the UPC number of this product?

    A: cola_data['code'] or cola_data['product']['_id']

  2. Q: What is the name of this product?

    A: cola_data['product']['product_name']

  3. Q: What is the amount of sodium in one serving of this product?

    A: cola_data['product']['nutriments']['sodium']