Python json data types

Python JSON

JSON is text, written with JavaScript object notation.

JSON in Python

Python has a built-in package called json , which can be used to work with JSON data.

Example

Parse JSON — Convert from JSON to Python

If you have a JSON string, you can parse it by using the json.loads() method.

Example

Convert from JSON to Python:

# the result is a Python dictionary:
print(y[«age»])

Convert from Python to JSON

If you have a Python object, you can convert it into a JSON string by using the json.dumps() method.

Example

Convert from Python to JSON:

# a Python object (dict):
x = «name»: «John»,
«age»: 30,
«city»: «New York»
>

# convert into JSON:
y = json.dumps(x)

# the result is a JSON string:
print(y)

You can convert Python objects of the following types, into JSON strings:

Example

Convert Python objects into JSON strings, and print the values:

print(json.dumps())
print(json.dumps([«apple», «bananas»]))
print(json.dumps((«apple», «bananas»)))
print(json.dumps(«hello»))
print(json.dumps(42))
print(json.dumps(31.76))
print(json.dumps(True))
print(json.dumps(False))
print(json.dumps(None))

When you convert from Python to JSON, Python objects are converted into the JSON (JavaScript) equivalent:

Python	JSON
dict	Object
list	Array
tuple	Array
str	String
int	Number
float	Number
True	true
False	false
None	null

Example

Convert a Python object containing all the legal data types:

Format the Result

The example above prints a JSON string, but it is not very easy to read, with no indentations and line breaks.

The json.dumps() method has parameters to make it easier to read the result:

Example

Use the indent parameter to define the numbers of indents:

You can also define the separators, default value is («, «, «: «), which means using a comma and a space to separate each object, and a colon and a space to separate keys from values:

Example

Use the separators parameter to change the default separator:

Order the Result

The json.dumps() method has parameters to order the keys in the result:

Example

Use the sort_keys parameter to specify if the result should be sorted or not:

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Working with JSON data in Python

JSON is a very popular data format. Its popularity is probably due to it’s simplicity, flexibility and ease of use. Python provides some pretty powerful tools that makes it very easy to work with JSON data.

Python has a builtin module called JSON which contains all the features you need to deal with exporting, importing and validating JSON data.

What is JSON?

JSON stands for JavaScript Object Notation. It comes from JavaScript, but can be used in any programming language. It can be used to transfer or store data in a simple human-readable format.

It is a subset of JavaScript (so it is executable with eval, but you should never ever do that, as it can lead to very serious security issues)

It is important to note, that JSON is not a concrete technology it is just a standard for describing data. So it does not define things like maximal string length, biggest available integer or floating point accuracy — however the underlying language or a certain implementation of a JSON parser will certainly have these kinds of limitations.

Why is JSON so popular?

1. Generating and parsing JSON is easy for machines
2. JSON is a human-readable data format
3. It is extremely simple
4. Despite of it’s simplicity, it’s still quite powerful and flexible

What does JSON data look like?

As I mentioned above, JSON is a subset of JavaScript, but it has some restrictions. Basically, you can define JSON objects the way you would define objects in JavaScript.

An example of a piece of JSON data:

 "exampleString": "hello", "exampleObject": "field": "value">, "exampleNumber": 1234, "exampleArray": ["aString", 1234, "field2": "value2">] >

Note, that the syntax is a bit stricter than in JavaScript:

• JSON objects cannot have field names without the surrounding double quotes ( is invalid)
• JSON strings must be enclosed in double quotes — single quotes are not allowed ( is invalid)
• Trailing commas after the last field are not allowed in JSON objects ( is invalid)

JSON data types

JSON defines four data types: string , number , object , array , and the special values of «true» , «false» and «null» . That’s all. Of course arrays and objects can contain strings, numbers or nested arrays and objects, so you can build arbitrarily complex data structures.

JSON strings

JSON strings consist of zero or more characters enclosed in double quotes.

JSON number

JSON numbers can be integers or decimals, the scientific notation is also allowed.

Examples: 123 , -10 , 3.14 , 1.23e-14

JSON object

Objects are a collection of key-value pairs. Keys should be enclosed in double quotes. Keys and values are separated by colons and the pairs are separated by commas. Values can be of any valid JSON type. The object is enclosed in curly braces.

"hello": "world", "numberField": 123>

JSON array

JSON arrays can contain zero or more items separated by commas. Items can be of any valid type.

Examples: [] , [«a»] , [1, 2, 3] , [«abc», 1234, , [«nested», «list»]]

Where is JSON used?

JSON can be used to both transfer and store data.

JSON web APIs — JSON data transfer in HTTP REST APIs

JSON is commonly used in REST APIs both in the request and the response of the body. The clients’ requests are usually marked with the application/json header. An http client can also indicate that it excepts a JSON response by using the Accept header.

POST /hello HTTP/1.1 Content-Type: application/json Accept: application/json

HTTP/1.1 200 OK Content-Type: application/json

NoSQL databases

JSON is commonly used for communicating with non-relational databases (such as MongoDB). NoSQL databases let you dynamically define the structure of your data, and JSON is perfect for the task because of its simplicity and flexibility.

JSON in Python — The JSON module

Working with JSON in Python is rather simple as Python has a builtin module that does all the heavy lifting for you. With the help of the json module you can parse and generate JSON-encoded strings and also read or write JSON encoded files directly.

Working with JSON strings

Exporting data to JSON format

You can turn basic Python data types into a JSON-encoded string with the help of json.dumps , the usage is pretty simple:

data =  "list": ["hello", "world"], "integer": 1234, "float": 3.14, "dir": "a": "b">, "bool": False, "null": None > import json json_encoded_data = json.dumps(data) print(json_encoded_data)

 "float": 3.14, "list": ["hello", "world"], "bool": false, "integer": 1234, "null": null, "dir": "a": "b"> >

Parsing a JSON string

The reverse — parsing a JSON-encoded string into Python objects can be done by using the json.loads method, like so:

json_encoded_data = ''' < "float": 3.14, "list": ["hello", "world"], "bool": false, "integer": 1234, "null": null, "dir": >''' import json data = json.loads(json_encoded_data) print(data)

 'float': 3.14, 'list': ['hello', 'world'], 'bool': False, 'integer': 1234, 'null': None, 'dir': 'a': 'b'> > 

Validating a JSON string

The Python json module does not have a dedicated way to validate a piece of JSON data, however you can use json.loads to do that. json.loads will raise a JSONDecodeError exception, so you can use that to determine whether or not a string contains properly formatted JSON.

For example, you can define the following function to validate JSON strings:

import json def is_valid_json(data: str) -> bool: try: json.loads(data) except json.JSONDecodeError: return False return True

This function accepts a string as its single argument and will return a boolean. It will try to load the string and if it is not a valid JSON, it will catch the raised exception, and return False . If the JSON is valid, no exception will be raised, so the return value will be True .

Working with JSON files in Python

The json module also makes it possible for you to work with JSON files directly. Instead of loads and dumps you can use the load and dump methods. These methods work directly on files — they take an extra argument, and instead of reading/writing strings in memory they will let you import/export JSON data from/to the files you pass.

Exporting data to a JSON file

Export JSON data can be done by using the json.dump function. It takes two arguments, the first is the Python object that you’d like to export, while the second is the file where you want to write the encoded data.

data =  "list": ["hello", "world"], "integer": 1234, "float": 3.14, "dir": "a": "b">, "bool": False, "null": None > import json with open('ouptut.json', 'w') as output_file: json_encoded_data = json.dump(data, output_file)

First we opened the file for writing and passed the file handle to json.dump as its second argument. output.json will contains something like (added whitespace for readability):

 "float": 3.14, "list": ["hello", "world"], "bool": false, "integer": 1234, "null": null, "dir": "a": "b"> >

Parsing a JSON file

Reading JSON data from a file to an in-memory Python object can be done very similarly — with the help of the json.load method.

This method takes a file as it’s argument — the file that you’d like to read from.

For example, to parse the file that we created in the previous example, we can write:

import json with open('ouptut.json', 'w') as input_file: data = json.load(input_file) print(data)

First we open the file for reading, and then pass the file handle to json.load

 'float': 3.14, 'list': ['hello', 'world'], 'bool': False, 'integer': 1234, 'null': None, 'dir': 'a': 'b'> >

Validating a JSON file

To validate that a file contains valid JSON data, we can use the json.load method and try to load the JSON contained in the file. On failure we can catch the JSONDecodeError raised by json.load . If no exception occurs, the file contains valid JSON.

import json def is_valid_json_file(input_file: str) -> bool: try: with open(input_file, 'r') as f: json.load(f) except json.JSONDecodeError: return False return True

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Python json data types

JSON (JavaScript Object Notation) is a text-based data format used for exchanging and storing data between web applications. It simplifies the data transmission process between different programming languages and platforms.

The JSON standard has become increasingly popular in recent years. It’s a simple and flexible way of representing data that can be easily understood and parsed by both humans and machines. JSON consists of key-value pairs enclosed in curly braces, separated by a colon.

Python provides various tools, libraries and methods for parsing and manipulating JSON data, making it a popular choice for data analysts, web developers, and data scientists.

In this guide, we’ll explore the syntax and data types of JSON, as well as the Python libraries and methods used for parsing JSON data, including more advanced options like JMESPath and ChompJS, which are very useful for web scraping data.

Reading JSON

One of the most common tasks when working with JSON data is to read its contents. Python provides several built-in libraries for reading JSON from files, APIs, and web applications. To read JSON data, you can use the built-in json module (JSON Encoder and Decoder) in Python.

The json module provides two methods, loads and load, that allow you to parse JSON strings and JSON files, respectively, to convert JSON into Python objects such as lists and dictionaries. Next is an example on how to convert JSON string to a Python object with the loads method.

import json json_input = ‘< "make": "Tesla", "model": "Model 3", "year": 2022, "color": "Red" >‘ json_data = json.loads(json_input) print(json_data) # Output:

Following, we display an example using the load method. Given a JSON file:

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