json — JSON encoder and decoder¶
JSON (JavaScript Object Notation), specified by RFC 7159 (which obsoletes RFC 4627) and by ECMA-404, is a lightweight data interchange format inspired by JavaScript object literal syntax (although it is not a strict subset of JavaScript 1 ).
Be cautious when parsing JSON data from untrusted sources. A malicious JSON string may cause the decoder to consume considerable CPU and memory resources. Limiting the size of data to be parsed is recommended.
json exposes an API familiar to users of the standard library marshal and pickle modules.
Encoding basic Python object hierarchies:
Specializing JSON object decoding:
Using json.tool from the shell to validate and pretty-print:
See Command Line Interface for detailed documentation.
JSON is a subset of YAML 1.2. The JSON produced by this module’s default settings (in particular, the default separators value) is also a subset of YAML 1.0 and 1.1. This module can thus also be used as a YAML serializer.
This module’s encoders and decoders preserve input and output order by default. Order is only lost if the underlying containers are unordered.
Basic Usage¶
Serialize obj as a JSON formatted stream to fp (a .write() -supporting file-like object ) using this conversion table .
If skipkeys is true (default: False ), then dict keys that are not of a basic type ( str , int , float , bool , None ) will be skipped instead of raising a TypeError .
The json module always produces str objects, not bytes objects. Therefore, fp.write() must support str input.
If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is.
If check_circular is false (default: True ), then the circular reference check for container types will be skipped and a circular reference will result in a RecursionError (or worse).
If allow_nan is false (default: True ), then it will be a ValueError to serialize out of range float values ( nan , inf , -inf ) in strict compliance of the JSON specification. If allow_nan is true, their JavaScript equivalents ( NaN , Infinity , -Infinity ) will be used.
If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t" ), that string is used to indent each level.
Changed in version 3.2: Allow strings for indent in addition to integers.
If specified, separators should be an (item_separator, key_separator) tuple. The default is (‘, ‘, ‘: ‘) if indent is None and (‘,’, ‘: ‘) otherwise. To get the most compact JSON representation, you should specify (‘,’, ‘:’) to eliminate whitespace.
Changed in version 3.4: Use (‘,’, ‘: ‘) as default if indent is not None .
If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError . If not specified, TypeError is raised.
If sort_keys is true (default: False ), then the output of dictionaries will be sorted by key.
To use a custom JSONEncoder subclass (e.g. one that overrides the default() method to serialize additional types), specify it with the cls kwarg; otherwise JSONEncoder is used.
Changed in version 3.6: All optional parameters are now keyword-only .
Unlike pickle and marshal , JSON is not a framed protocol, so trying to serialize multiple objects with repeated calls to dump() using the same fp will result in an invalid JSON file.
Serialize obj to a JSON formatted str using this conversion table . The arguments have the same meaning as in dump() .
Keys in key/value pairs of JSON are always of the type str . When a dictionary is converted into JSON, all the keys of the dictionary are coerced to strings. As a result of this, if a dictionary is converted into JSON and then back into a dictionary, the dictionary may not equal the original one. That is, loads(dumps(x)) != x if x has non-string keys.
Deserialize fp (a .read() -supporting text file or binary file containing a JSON document) to a Python object using this conversion table .
object_hook is an optional function that will be called with the result of any object literal decoded (a dict ). The return value of object_hook will be used instead of the dict . This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting).
object_pairs_hook is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict . This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority.
Changed in version 3.1: Added support for object_pairs_hook.
parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str) . This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal ).
parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str) . This can be used to use another datatype or parser for JSON integers (e.g. float ).
Changed in version 3.11: The default parse_int of int() now limits the maximum length of the integer string via the interpreter’s integer string conversion length limitation to help avoid denial of service attacks.
parse_constant, if specified, will be called with one of the following strings: ‘-Infinity’ , ‘Infinity’ , ‘NaN’ . This can be used to raise an exception if invalid JSON numbers are encountered.
Changed in version 3.1: parse_constant doesn’t get called on ‘null’, ‘true’, ‘false’ anymore.
To use a custom JSONDecoder subclass, specify it with the cls kwarg; otherwise JSONDecoder is used. Additional keyword arguments will be passed to the constructor of the class.
If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised.
Changed in version 3.6: All optional parameters are now keyword-only .
Changed in version 3.6: fp can now be a binary file . The input encoding should be UTF-8, UTF-16 or UTF-32.
Deserialize s (a str , bytes or bytearray instance containing a JSON document) to a Python object using this conversion table .
The other arguments have the same meaning as in load() .
If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised.
Changed in version 3.6: s can now be of type bytes or bytearray . The input encoding should be UTF-8, UTF-16 or UTF-32.
Changed in version 3.9: The keyword argument encoding has been removed.
Encoders and Decoders¶
Simple JSON decoder.
Performs the following translations in decoding by default:
It also understands NaN , Infinity , and -Infinity as their corresponding float values, which is outside the JSON spec.
object_hook, if specified, will be called with the result of every JSON object decoded and its return value will be used in place of the given dict . This can be used to provide custom deserializations (e.g. to support JSON-RPC class hinting).
object_pairs_hook, if specified will be called with the result of every JSON object decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict . This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority.
Changed in version 3.1: Added support for object_pairs_hook.
parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str) . This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal ).
parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str) . This can be used to use another datatype or parser for JSON integers (e.g. float ).
parse_constant, if specified, will be called with one of the following strings: ‘-Infinity’ , ‘Infinity’ , ‘NaN’ . This can be used to raise an exception if invalid JSON numbers are encountered.
If strict is false ( True is the default), then control characters will be allowed inside strings. Control characters in this context are those with character codes in the 0–31 range, including ‘\t’ (tab), ‘\n’ , ‘\r’ and ‘\0’ .
If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised.
Changed in version 3.6: All parameters are now keyword-only .
Return the Python representation of s (a str instance containing a JSON document).
JSONDecodeError will be raised if the given JSON document is not valid.
Decode a JSON document from s (a str beginning with a JSON document) and return a 2-tuple of the Python representation and the index in s where the document ended.
This can be used to decode a JSON document from a string that may have extraneous data at the end.
class json. JSONEncoder ( * , skipkeys = False , ensure_ascii = True , check_circular = True , allow_nan = True , sort_keys = False , indent = None , separators = None , default = None ) ¶
Extensible JSON encoder for Python data structures.
Supports the following objects and types by default:
int, float, int- & float-derived Enums
Changed in version 3.4: Added support for int- and float-derived Enum classes.
To extend this to recognize other objects, subclass and implement a default() method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise TypeError ).
If skipkeys is false (the default), a TypeError will be raised when trying to encode keys that are not str , int , float or None . If skipkeys is true, such items are simply skipped.
If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is.
If check_circular is true (the default), then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause a RecursionError ). Otherwise, no such check takes place.
If allow_nan is true (the default), then NaN , Infinity , and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats.
If sort_keys is true (default: False ), then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis.
If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t" ), that string is used to indent each level.
Changed in version 3.2: Allow strings for indent in addition to integers.
If specified, separators should be an (item_separator, key_separator) tuple. The default is (‘, ‘, ‘: ‘) if indent is None and (‘,’, ‘: ‘) otherwise. To get the most compact JSON representation, you should specify (‘,’, ‘:’) to eliminate whitespace.
Changed in version 3.4: Use (‘,’, ‘: ‘) as default if indent is not None .
If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError . If not specified, TypeError is raised.
Changed in version 3.6: All parameters are now keyword-only .
Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError ).
For example, to support arbitrary iterators, you could implement default() like this:
Return a JSON string representation of a Python data structure, o. For example:
Encode the given object, o, and yield each string representation as available. For example:
Exceptions¶
Subclass of ValueError with the following additional attributes:
The unformatted error message.
The JSON document being parsed.
The start index of doc where parsing failed.
The line corresponding to pos.
The column corresponding to pos.
New in version 3.5.
Standard Compliance and Interoperability¶
The JSON format is specified by RFC 7159 and by ECMA-404. This section details this module’s level of compliance with the RFC. For simplicity, JSONEncoder and JSONDecoder subclasses, and parameters other than those explicitly mentioned, are not considered.
This module does not comply with the RFC in a strict fashion, implementing some extensions that are valid JavaScript but not valid JSON. In particular:
Infinite and NaN number values are accepted and output;
Repeated names within an object are accepted, and only the value of the last name-value pair is used.
Since the RFC permits RFC-compliant parsers to accept input texts that are not RFC-compliant, this module’s deserializer is technically RFC-compliant under default settings.
Character Encodings¶
The RFC requires that JSON be represented using either UTF-8, UTF-16, or UTF-32, with UTF-8 being the recommended default for maximum interoperability.
As permitted, though not required, by the RFC, this module’s serializer sets ensure_ascii=True by default, thus escaping the output so that the resulting strings only contain ASCII characters.
Other than the ensure_ascii parameter, this module is defined strictly in terms of conversion between Python objects and Unicode strings , and thus does not otherwise directly address the issue of character encodings.
The RFC prohibits adding a byte order mark (BOM) to the start of a JSON text, and this module’s serializer does not add a BOM to its output. The RFC permits, but does not require, JSON deserializers to ignore an initial BOM in their input. This module’s deserializer raises a ValueError when an initial BOM is present.
The RFC does not explicitly forbid JSON strings which contain byte sequences that don’t correspond to valid Unicode characters (e.g. unpaired UTF-16 surrogates), but it does note that they may cause interoperability problems. By default, this module accepts and outputs (when present in the original str ) code points for such sequences.
Infinite and NaN Number Values¶
The RFC does not permit the representation of infinite or NaN number values. Despite that, by default, this module accepts and outputs Infinity , -Infinity , and NaN as if they were valid JSON number literal values:
In the serializer, the allow_nan parameter can be used to alter this behavior. In the deserializer, the parse_constant parameter can be used to alter this behavior.
Repeated Names Within an Object¶
The RFC specifies that the names within a JSON object should be unique, but does not mandate how repeated names in JSON objects should be handled. By default, this module does not raise an exception; instead, it ignores all but the last name-value pair for a given name:
The object_pairs_hook parameter can be used to alter this behavior.
Top-level Non-Object, Non-Array Values¶
The old version of JSON specified by the obsolete RFC 4627 required that the top-level value of a JSON text must be either a JSON object or array (Python dict or list ), and could not be a JSON null, boolean, number, or string value. RFC 7159 removed that restriction, and this module does not and has never implemented that restriction in either its serializer or its deserializer.
Regardless, for maximum interoperability, you may wish to voluntarily adhere to the restriction yourself.
Implementation Limitations¶
Some JSON deserializer implementations may set limits on:
the size of accepted JSON texts
the maximum level of nesting of JSON objects and arrays
the range and precision of JSON numbers
the content and maximum length of JSON strings
This module does not impose any such limits beyond those of the relevant Python datatypes themselves or the Python interpreter itself.
When serializing to JSON, beware any such limitations in applications that may consume your JSON. In particular, it is common for JSON numbers to be deserialized into IEEE 754 double precision numbers and thus subject to that representation’s range and precision limitations. This is especially relevant when serializing Python int values of extremely large magnitude, or when serializing instances of “exotic” numerical types such as decimal.Decimal .
Command Line Interface¶
The json.tool module provides a simple command line interface to validate and pretty-print JSON objects.
If the optional infile and outfile arguments are not specified, sys.stdin and sys.stdout will be used respectively:
Changed in version 3.5: The output is now in the same order as the input. Use the —sort-keys option to sort the output of dictionaries alphabetically by key.
Command line options¶
The JSON file to be validated or pretty-printed:
If infile is not specified, read from sys.stdin .
Write the output of the infile to the given outfile. Otherwise, write it to sys.stdout .
Sort the output of dictionaries alphabetically by key.
New in version 3.5.
Disable escaping of non-ascii characters, see json.dumps() for more information.
New in version 3.9.
Parse every input line as separate JSON object.
New in version 3.8.
Mutually exclusive options for whitespace control.
New in version 3.9.
Show the help message.
As noted in the errata for RFC 7159, JSON permits literal U+2028 (LINE SEPARATOR) and U+2029 (PARAGRAPH SEPARATOR) characters in strings, whereas JavaScript (as of ECMAScript Edition 5.1) does not.
# JSON данные
С самого начала JSON стал стандартом для обмена информацией.
Работа с JSON, это довольно распространенная задача, и, как и в случае большинства обычных задач, Python делает ее очень легкой.
Итак, мы используем JSON для хранения и обмена данными. Это не более чем стандартный формат, который сообщество использует для передачи данных. Имейте в виду, JSON — не единственный формат, доступный для такого рода работы, но XML и YAML, вероятно, единственные другие, которые стоит упомянуть.
# Краткая история JSON
Не удивительно, что JavaScript Object Notation (объектная нотация JavaScript) была вдохновлена подмножеством языка программирования JavaScript, связанным с литеральным синтаксисом объекта. Не беспокойтесь: JSON уже давно стал независимым от языка и существует в качестве своего собственного стандарта, поэтому, мы можем избегать JavaScript ради этой дискуссии.
В конечном счете, сообщество в целом приняло JSON, потому что людям и машинам легко создавать и понимать его.
# Посмотрим на JSON
JSON должен быть доступен для чтения любому, кто использовал язык Cи-стиля, а Python — язык Cи-стиля.
Как видите, JSON поддерживает примитивные типы, такие как строки и числа, а также вложенные списки и объекты.
Он похож на словарь Python! На данный момент это довольно универсальная система обозначений объектов.
# Подключение JSON
Python поставляется со встроенным пакетом json для кодирования и декодирования данных JSON.
Просто подключив вверху вашего файла:
# Терминология
Процесс кодирования JSON обычно называется сериализацией. Этот термин относится к преобразованию данных в серию байтов (следовательно, последовательных) для хранения или передачи по сети.
десериализация — это взаимный процесс декодирования данных, которые были сохранены или добавлены в стандарте JSON.
Это звучит довольно технически. Определенно. Но на самом деле все, о чем мы здесь говорим — это чтение и письмо. Думайте об этом так: кодирование предназначено для записи данных на диск, а декодирование — для чтения данных в память.
# Сериализация JSON
Что происходит после того, как компьютер обрабатывает много информации? Нужно взять дамп данных. Соответственно, библиотека json предоставляет метод dump() для записи данных в файлы. Существует также метод dumps() (произносится как «dump-s») для записи в строку Python.
Простые объекты Python переводятся в JSON в соответствии с довольно интуитивным преобразованием.
# Пример сериализации
Представьте, что вы работаете с объектом Python в памяти, который выглядит примерно так:
Крайне важно сохранить эту информацию на диске, поэтому запишем ее в файл.
Создадим файл с именем data_file.json и откроем его в режиме записи. (Файлы JSON обычно заканчиваются расширением .json.)
Обратите внимание, что dump() принимает два позиционных аргумента: (1) объект данных для сериализации и (2) файлоподобный объект, в который будут записаны байты.
Или, если хотите продолжать использовать эти сериализованные данные JSON в своей программе, вы можете записать их в собственный объект (переменную) Python.
Обратите внимание, что файлоподобный объект отсутствует, так как вы фактически не записываете на диск. В остальном dumps() похожа на dump() .
# Некоторые полезные аргументы ключевого слова
Помните, что JSON должен легко читаться людьми, но читаемого синтаксиса недостаточно, если все это сжато вместе. Кроме того, вам может быть проще читать код, когда он отформатирован по вашему вкусу.
Методы dump() и dumps() используют одинаковые ключевые аргументы.
Первый вариант, который большинство людей хотят изменить, — это пробел. Вы можете использовать аргумент ключевого слова indent , чтобы указать размер отступа для вложенных структур. Проверьте разницу, используя данные, которые мы определили выше, добавив новый параметр и посмотрите результат:
Другой вариант форматирования — аргумент ключевого слова separators . По умолчанию это две строки разделителей (",", ":") , но распространенной альтернативой для компактного JSON является (",", ":") . Посмотрите на образец JSON еще раз, чтобы увидеть, где находятся разделители.
# Упражнения
- Создайте словарь в файле с кодом, с рецептом и перечнем продуктов в нем. Сохраните исходные значения переменной в *.json файл.
# Упражнения GUI
- Создайте программу с текстовым окном. В текстовом окне выведите пример рецепта в виде словаря. Словарь изначально храниться в значении переменной в коде.
- Добавьте кнопку сохранения файла, при нажатии на которую вызовется диалоговое окно выбора места сохранения. При выборе места сохранения файл будет сохраняться в виде .json.
# Десериализация JSON
В библиотеке json вы найдете load() и loads() для преобразования закодированных данных JSON в объекты Python.
Как и в случае с сериализацией, существует простая таблица преобразования для десериализации, которая выглядит так:
Технически, это преобразование не является совершенной противоположностью таблице сериализации. По сути, это означает, что если вы кодируете объект сейчас, а потом декодируете его позже, вы не сможете получить точно такой же объект обратно.
В действительности, это больше похоже на то, как один друг переводит что-то c английского на русский, а другой — обратно на английский. В любом случае, простейшим примером будет кодирование кортежа и получение списка после декодирования, например, так:
# Пример десериализации
На этот раз представьте, что на диске хранятся некоторые данные, которыми вы хотели бы манипулировать в памяти. Откройте существующий data_file.json в режиме чтения.
Здесь все довольно просто, но имейте в виду, что результат этого метода может вернуть любой из разрешенных типов данных из таблицы преобразования. Это важно только в том случае, если вы загружаете данные, которые раньше не видели. В большинстве случаев корневым объектом будет dict или list .
Если вы извлекли данные JSON из другой программы или иным образом получили строку данных в формате JSON, вы можете легко десериализовать это с помощью load() , которая естественным образом загружается из строки:
Python JSON – How to Convert a String to JSON
Dionysia Lemonaki
In this tutorial you’ll learn the basics of JSON – what it is, where it is most commonly used, and its syntax.
You’ll also see how to convert a string to JSON in Python.
Let’s get started!
What is JSON?
JSON stands for JavaScript Object Notation.
It is a data format that’s used for storing and transferring information for web applications.
JSON was inspired by the JavaScript programming language, but it’s not tied to only one language.
Most modern programming languages have libraries for parsing and generating JSON data.
Where is JSON used?
JSON is mostly used for sending and receiving data between a server and a client, where the client is a webpage or web application.
It’s a much more solid format to use during the request-response cycle web applications use when connecting over a network. This is compared to the complicated and less compact XML, which was the format of choice years ago.
Basic JSON syntax
In JSON, data is written in key-value pairs, like so:
Data is enclosed in double quotation marks and the key-value pair is separated by a colon.
There can be more than one key-value pair and each one is separated by a comma:
The example above showed an object, a collection of multiple key-value pairs.
Objects are inside curly braces:
You can also create arrays, an ordered list of values, with JSON. In that case, arrays are contained inside square brackets:
How to work with JSON data in Python
Include the JSON module for Python
To use JSON with Python, you’ll first need to include the JSON module at the top of your Python file. This comes built-in to Python and is part of the standard library.
So, say you have a file named demo.py . At the top you would add the following line:
Use the json.loads() function
If you have JSON string data in your program like so:
you can turn it into JSON in Python using the json.loads() function.
The json.loads() function accepts as input a valid string and converts it to a Python dictionary.
This process is called deserialization – the act of converting a string to an object.
You can then access each individual item, like you would when using a Python dictionary:
Let’s take another example:
- Take some JSON string data:
- Use the json.loads() function to convert a string to an object:
- Access the data:
Conclusion
And there you have it – you now know the basics of using JSON in Python.
If you want to learn more about Python, freeCodeCamp has a Python Certification which takes you from the fundamentals such as variables, loops, and functions to more advanced concepts such as data structures. In the end you’ll also build 5 projects.
Encoding and Decoding JSON with Python — A Cheatsheet
Conversion of json string (or json file) to python and vice versa is often a confusing endeavor. This article can serve as a cheatsheet.
In this short post, I provide a highlight of the conversion functions from json to python and vice versa. The functions are:
- json.loads(jsonString) — json string to python object
- json.dumps(pythonObject) — python object to json string
- json.load(fileHandler) — json file to python object — python object from file
- json.dump(jsonString or pythonObject, fileHandler) — json string or python object to json file
When you want to access json elements using their keys, you need to convert a json string or a json file into a python dictionary with json.loads(json string) or json.dumps(file handler), respectively. Note the s in loads — which perhaps stands for string, thus loads == loadString.
Similarly, when you want to convert a python dictionary to a json string, e.g., for string processing, or want to save it as a json file, you use json.load(python_dict) or json.dump(json string or python object, file handler), respectively. The following code snippets demonstrates these functions. But first of all, you need to import the required library, i.e., json.
- json.loads()
the above code produces:
2. json.dumps()
the above code snippet produces:
3. json.dump()
4. json.load()
The above code snippet produces
I hope this article give a summary of the four functions that are often used for encoding and decoding json data. Drop me a question or feedback, if you have one, in the comment section.