Why can't Python parse this JSON data? [closed]

I have this JSON in a file:

{
    "maps": [
        {
            "id": "blabla",
            "iscategorical": "0"
        },
        {
            "id": "blabla",
            "iscategorical": "0"
        }
    ],
    "masks": [
        "id": "valore"
    ],
    "om_points": "value",
    "parameters": [
        "id": "valore"
    ]
}

I wrote this script to print all of the JSON data:

import json
from pprint import pprint

with open('data.json') as f:
    data = json.load(f)

pprint(data)

This program raises an exception, though:

Traceback (most recent call last):
  File "<pyshell#1>", line 5, in <module>
    data = json.load(f)
  File "/usr/lib/python3.5/json/__init__.py", line 319, in loads
    return _default_decoder.decode(s)
  File "/usr/lib/python3.5/json/decoder.py", line 339, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/usr/lib/python3.5/json/decoder.py", line 355, in raw_decode
    obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting ',' delimiter: line 13 column 13 (char 213)

How can I parse the JSON and extract its values?

Solution 1:

Your data is not valid JSON format. You have [] when you should have {}:

• [] are for JSON arrays, which are called list in Python
• {} are for JSON objects, which are called dict in Python

Here's how your JSON file should look:

{
    "maps": [
        {
            "id": "blabla",
            "iscategorical": "0"
        },
        {
            "id": "blabla",
            "iscategorical": "0"
        }
    ],
    "masks": {
        "id": "valore"
    },
    "om_points": "value",
    "parameters": {
        "id": "valore"
    }
}

Then you can use your code:

import json
from pprint import pprint

with open('data.json') as f:
    data = json.load(f)

pprint(data)

With data, you can now also find values like so:

data["maps"][0]["id"]
data["masks"]["id"]
data["om_points"]

Try those out and see if it starts to make sense.

Solution 2:

Your data.json should look like this:

{
 "maps":[
         {"id":"blabla","iscategorical":"0"},
         {"id":"blabla","iscategorical":"0"}
        ],
"masks":
         {"id":"valore"},
"om_points":"value",
"parameters":
         {"id":"valore"}
}

Your code should be:

import json
from pprint import pprint

with open('data.json') as data_file:    
    data = json.load(data_file)
pprint(data)

Note that this only works in Python 2.6 and up, as it depends upon the with-statement. In Python 2.5 use from __future__ import with_statement, in Python <= 2.4, see Justin Peel's answer, which this answer is based upon.

You can now also access single values like this:

data["maps"][0]["id"]  # will return 'blabla'
data["masks"]["id"]    # will return 'valore'
data["om_points"]      # will return 'value'

Solution 3:

Justin Peel's answer is really helpful, but if you are using Python 3 reading JSON should be done like this:

with open('data.json', encoding='utf-8') as data_file:
    data = json.loads(data_file.read())

Note: use json.loads instead of json.load. In Python 3, json.loads takes a string parameter. json.load takes a file-like object parameter. data_file.read() returns a string object.

To be honest, I don't think it's a problem to load all json data into memory in most cases. I see this in JS, Java, Kotlin, cpp, rust almost every language I use. Consider memory issue like a joke to me :)

On the other hand, I don't think you can parse json without reading all of it.

Solution 4:

data = []
with codecs.open('d:\output.txt','rU','utf-8') as f:
    for line in f:
       data.append(json.loads(line))

Solution 5:

"Ultra JSON" or simply "ujson" can handle having [] in your JSON file input. If you're reading a JSON input file into your program as a list of JSON elements; such as, [{[{}]}, {}, [], etc...] ujson can handle any arbitrary order of lists of dictionaries, dictionaries of lists.

You can find ujson in the Python package index and the API is almost identical to Python's built-in json library.

ujson is also much faster if you're loading larger JSON files. You can see the performance details in comparison to other Python JSON libraries in the same link provided.