15.5. Parsing JSON

We construct our JSON by nesting dictionaries and lists as needed. In this example, we represent a list of users where each user is a set of key-value pairs (i.e., a dictionary). So we have a list of dictionaries.

In the following program, we use the built-in json library to parse the JSON and read through the data. Compare this closely to the equivalent XML data and code on the previous page. The JSON has less detail, so we must know in advance that we are getting a list and that the list is of users and each user is a set of key-value pairs. The JSON is more succinct (an advantage) but also is less self-describing (a disadvantage).

If you compare the code to extract data from the parsed JSON and XML you will see that what we get from json.loads() is a Python list which we traverse with a for loop, and each item within that list is a Python dictionary. Once the JSON has been parsed, we can use the Python index operator to extract the various bits of data for each user. We don’t have to use the JSON library to dig through the parsed JSON, since the returned data is simply native Python structures.

The output of this program is exactly the same as the XML version from earlier.

User count: 2
Name Chuck
Id 001
Attribute 2
Name Brent
Id 009
Attribute 7

In general, there is an industry trend away from XML and towards JSON for web services. Because the JSON is simpler and more directly maps to native data structures we already have in programming languages, the parsing and data extraction code is usually simpler and more direct when using JSON. But XML is more self-descriptive than JSON and so there are some applications where XML retains an advantage. For example, most word processors store documents internally using XML rather than JSON.

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