Storing and Loading Data with JSON

We’ve already learned about pickle, so why do we need another way to (de)serialize Python objects to(from) disk or a network connection? There are three major reasons to prefer JSON over pickle:

  • When you’re unpickling data, you’re essentially allowing your data source to execute arbitrary Python commands. If the data is trustworthy (say stored in a sufficiently protected directory), that may not be a problem, but it’s often really easy to accidentally leave a file unprotected (or read something from network). In these cases, you want to load data, and not execute potentially malicious Python code!
  • Pickled data is not easy to read, and virtually impossible to write for humans. For example, the pickled version of {"answer": [42]} looks like this:

In contrast, the JSON representation of {"answer": [42]} is {"answer": [42]}. If you can read Python, you can read JSON; since all JSON is valid Python code!

So how do you get the JSON representation of an object? It’s simple, just call json.dumps:

import json
obj = {u"answer": [42.2], u"abs": 42}
# output:  {"answer": [42.2], "abs": 42}

Often, you want to write to a file or network stream. In both Python 2.x and 3.x you can call dump to do that, but in 3.x the output must be a character stream, whereas 2.x expects a byte stream.

Let’s look how to load what we wrote. Fittingly, the function to load is called loads (to load from a string) / load (to load from a stream):

import json
obj_json = u'{"answer": [42.2], "abs": 42}'
obj = json.loads(obj_json)

When the objects we load and store grow larger, we puny humans often need some hints on where a new sub-object starts. To get these, simply pass an indent size, like this:

import json
obj = {u"answer": [42.2], u"abs": 42}
print(json.dumps(obj, indent=4))

Now, the output will be a beautiful

    "abs": 42, 
    "answer": [

I often use this indentation feature to debug complex data structures.

The price of JSON’s interoperability is that we cannot store arbitrary Python objects. In fact, JSON can only store the following objects:

  • character strings
  • numbers
  • booleans (True/False)
  • None
  • lists
  • dictionaries with character string keys

Every object that’s not one of these must be converted – that includes every object of a custom class. Say we have an object alice as follows:

class User(object):
    def __init__(self, name, password): = name
        self.password = password
alice = User('Alice A. Adams', 'secret')

then converting this object to JSON will fail:

>>> import json
>>> json.dumps(alice)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.3/json/", line 236, in dumps
    return _default_encoder.encode(obj)
  File "/usr/lib/python3.3/json/", line 191, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "/usr/lib/python3.3/json/", line 249, in iterencode
    return _iterencode(o, 0)
  File "/usr/lib/python3.3/json/", line 173, in default
    raise TypeError(repr(o) + " is not JSON serializable")
TypeError: <__main__.User object at 0x7f2eccc88150> is not JSON serializable

Fortunately, there is a simple hook for conversion: Simply define a default method:

def jdefault(o):
    return o.__dict__
print(json.dumps(alice, default=jdefault))
# outputs: {"password": "secret", "name": "Alice A. Adams"}

o.__dict__ is a simple catch-all for user-defined objects, but we can also add support for other objects. For example, let’s add support for sets by treating them like lists:

def jdefault(o):
    if isinstance(o, set):
        return list(o)
    return o.__dict__

pets = set([u'Tiger', u'Panther', u'Toad'])
print(json.dumps(pets, default=jdefault))
# outputs: ["Tiger", "Panther", "Toad"]

For more options and details (ensure_ascii and sort_keys may be interesting options to set), have a look at the official documentation for JSON. JSON is available by default in Python 2.6 and newer, before that you can use simplejson as a fallback.

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