JSON Module

JSON Module in Python is utilized by entering specific code lines and performing required functions. Learn More about this module here.

JSON Module: Storing data in a file

The following snippet encodes the data stored in d into JSON and stores it in a file (replace filename with the actual name of the file).

import json
d = {
'foo': 'bar',
'alice': 1,
'wonderland': [1, 2, 3]
with open(filename, 'w') as f:
json.dump(d, f)

JSON Module: Retrieving data from a file

The following snippet opens a JSON encoded file (replace filename with the actual name of the file) and returns the object that is stored in the file.

import json
with open(filename, 'r') as f:
d = json.load(f)

JSON Module: Formatting JSON output

Let’s say we have the following data:

data = {"cats": [{"name": "Tubbs", "color": "white"}, {"name": "Pepper", "color": "black"}]}

Just dumping this as JSON does not do anything special here:

{"cats": [{"name": "Tubbs", "color": "white"}, {"name": "Pepper", "color": "black"}]}

Setting indentation to get prettier output

If we want pretty printing, we can set an indent size:

print(json.dumps(data, indent=2))
"cats": [
"name": "Tubbs",
"color": "white"
"name": "Pepper",
"color": "black"

Sorting keys alphabetically to get consistent output

By default the order of keys in the output is undefined. We can get them in alphabetical order to make sure we always get the same output:

print(json.dumps(data, sort_keys=True))
{"cats": [{"color": "white", "name": "Tubbs"}, {"color": "black", "name": "Pepper"}]}

Getting rid of whitespace to get compact output

We might want to get rid of the unnecessary spaces, which is done by setting separator strings different from the default ‘, ‘ and ‘: ‘:

print(json.dumps(data, separators=(',', ':')))

JSON Module: load vs loads, dump vs dumps

The json module contains functions for both reading and writing to and from unicode strings, and reading and writing to and from files. These are differentiated by a trailing s in the function name. In these examples we use a StringIO object, but the same functions would apply for any file-like object.

Here we use the string-based functions:

import json
data = {u"foo": u"bar", u"baz": []}
json_string = json.dumps(data)
u'{"foo": "bar", "baz": []}' json.loads(json_string)
{u"foo": u"bar", u"baz": []}

And here we use the file-based functions:

import json
from io import StringIO
json_file = StringIO()
data = {u"foo": u"bar", u"baz": []}
json.dump(data, json_file)
json_file.seek(0) # Seek back to the start of the file before reading json_file_content = json_file.read()

u'{“foo”: “bar”, “baz”: []}’

json_file.seek(0) # Seek back to the start of the file before reading json.load(json_file)

{u”foo”: u”bar”, u”baz”: []}

As you can see the main difference is that when dumping json data you must pass the file handle to the function, as opposed to capturing the return value. Also worth noting is that you must seek to the start of the file before reading or writing, in order to avoid data corruption. When opening a file the cursor is placed at position 0, so the below would also work:

import json
json_file_path = './data.json'
data = {u"foo": u"bar", u"baz": []}
with open(json_file_path, 'w') as json_file:
json.dump(data, json_file)
with open(json_file_path) as json_file:
json_file_content = json_file.read()

u'{“foo”: “bar”, “baz”: []}’

with open(json_file_path) as json_file:

{u”foo”: u”bar”, u”baz”: []}

Having both ways of dealing with json data allows you to idiomatically and efficiently work with formats which build upon json, such as pyspark’s json-per-line:

loading from a file

data = [json.loads(line) for line in open(file_path).splitlines()]

dumping to a file

with open(file_path, 'w') as json_file:
for item in data:
json.dump(item, json_file)

JSON Module: Calling json.tool from the command line to pretty-print JSON output

Given some JSON file “foo.json” like:

{"foo": {"bar": {"baz": 1}}}

we can call the module directly from the command line (passing the filename as an argument) to pretty-print it:

python -m json.tool foo.json
"foo": { "bar": {
"baz": 1

The module will also take input from STDOUT, so (in Bash) we equally could do:

$ cat foo.json | python -m json.tool

JSON encoding custom objects

If we just try the following:

import json
from datetime import datetime
data = {'datetime': datetime(2016, 9, 26, 4, 44, 0)} print(json.dumps(data))
we get an error saying TypeError: datetime.datetime(2016, 9, 26, 4, 44) is not JSON serializable.

To be able to serialize the datetime object properly, we need to write custom code for how to convert it:

class DatetimeJSONEncoder(json.JSONEncoder):
def default(self, obj):
return obj.isoformat()
except AttributeError:
obj has no isoformat method; let the builtin JSON encoder handle it return super(DatetimeJSONEncoder, self).default(obj)
and then use this encoder class instead of json.dumps:
encoder = DatetimeJSONEncoder()
prints {“datetime”: “2016-09-26T04:44:00”}

Creating JSON from Python dict

import json
d = {
'foo': 'bar',
'alice': 1,
'wonderland': [1, 2, 3]

The above snippet will return the following:

'{"wonderland": [1, 2, 3], "foo": "bar", "alice": 1}'

JSON Module: Creating Python dict from JSON

import json
s = '{"wonderland": [1, 2, 3], "foo": "bar", "alice": 1}' json.loads(s)

The above snippet will return the following:

{u'alice': 1, u'foo': u'bar', u'wonderland': [1, 2, 3]}

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