How To Create Json Data In Python

Introduction of JSON in Python :

The full form of JSON is JavaScript Object Notation. It means that a script (executable) file which is made of text in a programming language, is used to store and transfer the data. Python supports JSON through a built-in package called JSON. To use this feature, we import the JSON package in Python script. The text in JSON is done through quoted-string which contains the value in key-value mapping within { }. It is similar to the dictionary in Python. JSON shows an API similar to users of Standard Library marshal and pickle modules and Python natively supports JSON features. For Example

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Python3

import json

a = { "name" : "John" ,

"age" : 31 ,

"Salary" : 25000 }

b = json.dumps(a)

print (b)

Output:

{"age": 31, "Salary": 25000, "name": "John"}

As you can see, JSON supports primitive types, like strings and numbers, as well as nested lists, tuples, and objects

Python3

import json

print (json.dumps([ 'Welcome' , "to" , "GeeksforGeeks" ]))

print (json.dumps(( "Welcome" , "to" , "GeeksforGeeks" )))

print (json.dumps( "Hi" ))

print (json.dumps( 123 ))

print (json.dumps( 23.572 ))

print (json.dumps( True ))

print (json.dumps( False ))

print (json.dumps( None ))

Output:

["Welcome", "to", "GeeksforGeeks"] ["Welcome", "to", "GeeksforGeeks"] "Hi" 123 23.572 true false null

Serializing JSON :

The process of encoding JSON is usually called serialization. This term refers to the transformation of data into a series of bytes (hence serial) to be stored or transmitted across a network. To handle the data flow in a file, the JSON library in Python uses dump() function to convert the Python objects into their respective JSON object, so it makes easy to write data to files. See the following table given below.

Python object	JSON object
dict	object
list, tuple	array
str	string
int, long, float	numbers
True	true
False	false
None	null

Serialization Example :

Consider the given example of a Python object.

Python3

var = {

"Subjects" : {

"Maths" : 85 ,

"Physics" : 90

}

Using Python's context manager, create a file named Sample.json and open it with write mode.

Python3

with open ( "Sample.json" , "w" ) as p:

json.dump(var, p)

Here, the dump() takes two arguments first, the data object to be serialized, and second the object to which it will be written(Byte format).

Deserializing JSON :

Deserialization is the opposite of Serialization, i.e. conversion of JSON objects into their respective Python objects. The load() method is used for it. If you have used JSON data from another program or obtained it as a string format of JSON, then it can easily be deserialized with load(), which is usually used to load from string, otherwise, the root object is in a list or dict.

Python3

with open ( "Sample.json" , "r" ) as read_it:

data = json.load(read_it)

Deserialization Example :

Python3

json_var =

var = json.loads(json_var)

Encoding and Decoding :

Encoding is defined as converting the text or values into an encrypted form that can only be used by the desired user through decoding it. Here encoding and decoding is done for JSON (object)format. Encoding is also known as Serialization and Decoding is known as Deserialization. Python has a popular package for this operation. This package is known as Demjson. To install it follow the steps below.

For Windows:

pip install demjson

For Ubuntu:

          sudo apt-get update  sudo apt-get install python-demjson

Encoding : The encode() function is used to convert the python object into a JSON string representation.

Syntax: demjson.encode(self, obj, nest_level=0)

Example 1: Encoding using demjson package.

Python3

var = [{ "Math" : 50 , "physics" : 60 , "Chemistry" : 70 }]

print (demjson.encode(var))

Output:

          [{"Chemistry":70, "Math":50, "physics":60}]

Decoding: The decode() function is used to convert the JSON object into python-format type.

Syntax : demjson.decode(self, obj)

Example 2: Decoding using demjson package

Python3

var = '{"a":0, "b":1, "c":2, "d":3, "e":4}'

text = demjson.decode(var)

Output:

{'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4}

Example 3: Encoding using iterencode package

Python3

json.JSONEncoder().encode({ "foo" : [ "bar" ]})

'{"foo": ["bar"]}'

for i in json.JSONEncoder().iterencode(bigobject):

mysocket.write(i)

Example 4: Encoding and Decoding using dumps() and loads().

Python3

import json

var = { 'age' : 31 , 'height' = 6 }

x = json.dumps(var)

y = json.loads(var)

print (x)

print (y)

with open ( "any_file.json" , "r" ) as readit:

x = json.load(readit)

print (x)

Command-Line Usage

The JSON library can also be used from the command-line, to validate and pretty-print your JSON.

$ echo "{ \"name\": \"Monty\", \"age\": 45 }"

Searching through JSON with JMESPath

JMESPath is a query language for JSON. It allows you to easily obtain the data you need from a JSON document. If you ever worked with JSON before, you probably know that it's easy to get a nested value. For example, doc["person"]["age"] will get you the nested value for age in a document.

First, install jmespath :

$ pip3 install jmespath

Real-World Example :

Let us take a real-life example of the implementation of the JSON in python. A good source for practice purposes is JSON_placeholder, it provides a great API requests package which we will be using in our example. To get started, follow these simple steps. Open Python IDE or CLI and create a new script file, name it sample.py.

Python3

import requests

import json

var = json.loads(res.text)

var

def find(todo):

check = todo[ "completed" ]

max_var = todo[ "userId" ] in users

return check and max_var

Value = list ( filter (find, todos))

with open ( "sample.json" , "w" ) as data:

Value = list ( filter (keep, todos))

json.dump(Value, data, indent = 2 )

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