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.
How Do You Use JSON in Python: Let’s Find Out — Codefather
The Python json module allows to use data in JSON format in your applications. Do you want to convert a Python object into JSON or viceversa? This is the right module to do that.
The Python json module provides functions to convert a Python object into JSON (dump() and dumps()), this conversion is called serialization or encoding. The json module also provides…
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:mod:`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.
:mod:`json` exposes an API familiar to users of the standard library :mod:`marshal` and :mod:`pickle` modules.
Encoding basic Python object hierarchies:
Specializing JSON object decoding:
Using :mod:`json.tool` from the shell to validate and pretty-print:
See :ref:`json-commandline` 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.
Encoders and Decoders
Simple JSON decoder.
Performs the following translations in decoding by default:
| JSON | Python |
|---|---|
| object | dict |
| array | list |
| string | str |
| number (int) | int |
| number (real) | float |
| true | True |
| false | False |
| null | None |
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 :class:`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 :class:`dict` . This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority.
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. :class:`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. :class:`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 :exc:`JSONDecodeError` will be raised.
Extensible JSON encoder for Python data structures.
Supports the following objects and types by default:
| Python | JSON |
|---|---|
| dict | object |
| list, tuple | array |
| str | string |
| int, float, int- & float-derived Enums | number |
| True | true |
| False | false |
| None | null |
To extend this to recognize other objects, subclass and implement a :meth:`default` method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise :exc:`TypeError` ).
If skipkeys is false (the default), a :exc:`TypeError` will be raised when trying to encode keys that are not :class:`str` , :class:`int` , :class:`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 :exc:`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 :exc:`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.
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.
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 :exc:`TypeError` . If not specified, :exc:`TypeError` is raised.
Subclass of :class:`dict` that also supports attribute style dotted access.
Attribute style access only works for keys that are valid attribute names. In contrast, dictionary style access works for all keys. For example, d.two words contains a space and is not syntactically valid Python, so d[«two words»] should be used instead.
If a key has the same name as a dictionary method, then a dictionary lookup finds the key and an attribute lookup finds the method:
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, :class:`JSONEncoder` and :class:`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.
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 :class:`Unicode strings <str>` , 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 :exc:`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 :class:`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 :class:`dict` or :class:`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.
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.
Working With JSON Data in Python
Since its inception, JSON has quickly become the de facto standard for information exchange. Chances are you’re here because you need to transport some data from here to there. Perhaps you’re gathering information through an API or storing your data in a document database. One way or another, you’re up to your neck in JSON, and you’ve got to Python your way out.
Luckily, this is a pretty common task, and—as with most common tasks—Python makes it almost disgustingly easy. Have no fear, fellow Pythoneers and Pythonistas. This one’s gonna be a breeze!
So, we use JSON to store and exchange data? Yup, you got it! It’s nothing more than a standardized format the community uses to pass data around. Keep in mind, JSON isn’t the only format available for this kind of work, but XML and YAML are probably the only other ones worth mentioning in the same breath.
Free PDF Download: Python 3 Cheat Sheet
A (Very) Brief History of JSON
Not so surprisingly, JavaScript Object Notation was inspired by a subset of the JavaScript programming language dealing with object literal syntax. They’ve got a nifty website that explains the whole thing. Don’t worry though: JSON has long since become language agnostic and exists as its own standard, so we can thankfully avoid JavaScript for the sake of this discussion.
Ultimately, the community at large adopted JSON because it’s easy for both humans and machines to create and understand.
Look, it’s JSON!
Get ready. I’m about to show you some real life JSON—just like you’d see out there in the wild. It’s okay: JSON is supposed to be readable by anyone who’s used a C-style language, and Python is a C-style language…so that’s you!
As you can see, JSON supports primitive types, like strings and numbers, as well as nested lists and objects.
Wait, that looks like a Python dictionary! I know, right? It’s pretty much universal object notation at this point, but I don’t think UON rolls off the tongue quite as nicely. Feel free to discuss alternatives in the comments.
Whew! You survived your first encounter with some wild JSON. Now you just need to learn how to tame it.
Python Supports JSON Natively!
Python comes with a built-in package called json for encoding and decoding JSON data.
Just throw this little guy up at the top of your file:
A Little Vocabulary
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. You may also hear the term marshaling, but that’s a whole other discussion. Naturally, deserialization is the reciprocal process of decoding data that has been stored or delivered in the JSON standard.
Yikes! That sounds pretty technical. Definitely. But in reality, all we’re talking about here is reading and writing. Think of it like this: encoding is for writing data to disk, while decoding is for reading data into memory.
Serializing JSON
What happens after a computer processes lots of information? It needs to take a data dump. Accordingly, the json library exposes the dump() method for writing data to files. There is also a dumps() method (pronounced as “dump-s”) for writing to a Python string.
Simple Python objects are translated to JSON according to a fairly intuitive conversion.
| Python | JSON |
|---|---|
| dict | object |
| list , tuple | array |
| str | string |
| int , long , float | number |
| True | true |
| False | false |
| None | null |
A Simple Serialization Example
Imagine you’re working with a Python object in memory that looks a little something like this:
It is critical that you save this information to disk, so your mission is to write it to a file.
Using Python’s context manager, you can create a file called data_file.json and open it in write mode. (JSON files conveniently end in a .json extension.)
Note that dump() takes two positional arguments: (1) the data object to be serialized, and (2) the file-like object to which the bytes will be written.
Or, if you were so inclined as to continue using this serialized JSON data in your program, you could write it to a native Python str object.
Notice that the file-like object is absent since you aren’t actually writing to disk. Other than that, dumps() is just like dump() .
Hooray! You’ve birthed some baby JSON, and you’re ready to release it out into the wild to grow big and strong.
Some Useful Keyword Arguments
Remember, JSON is meant to be easily readable by humans, but readable syntax isn’t enough if it’s all squished together. Plus you’ve probably got a different programming style than me, and it might be easier for you to read code when it’s formatted to your liking.
NOTE: Both the dump() and dumps() methods use the same keyword arguments.
The first option most people want to change is whitespace. You can use the indent keyword argument to specify the indentation size for nested structures. Check out the difference for yourself by using data , which we defined above, and running the following commands in a console:
Another formatting option is the separators keyword argument. By default, this is a 2-tuple of the separator strings («, «, «: «) , but a common alternative for compact JSON is («,», «:») . Take a look at the sample JSON again to see where these separators come into play.
There are others, like sort_keys , but I have no idea what that one does. You can find a whole list in the docs if you’re curious.
Deserializing JSON
Great, looks like you’ve captured yourself some wild JSON! Now it’s time to whip it into shape. In the json library, you’ll find load() and loads() for turning JSON encoded data into Python objects.
Just like serialization, there is a simple conversion table for deserialization, though you can probably guess what it looks like already.
| JSON | Python |
|---|---|
| object | dict |
| array | list |
| string | str |
| number (int) | int |
| number (real) | float |
| true | True |
| false | False |
| null | None |
Technically, this conversion isn’t a perfect inverse to the serialization table. That basically means that if you encode an object now and then decode it again later, you may not get exactly the same object back. I imagine it’s a bit like teleportation: break my molecules down over here and put them back together over there. Am I still the same person?
In reality, it’s probably more like getting one friend to translate something into Japanese and another friend to translate it back into English. Regardless, the simplest example would be encoding a tuple and getting back a list after decoding, like so:
A Simple Deserialization Example
This time, imagine you’ve got some data stored on disk that you’d like to manipulate in memory. You’ll still use the context manager, but this time you’ll open up the existing data_file.json in read mode.
Things are pretty straightforward here, but keep in mind that the result of this method could return any of the allowed data types from the conversion table. This is only important if you’re loading in data you haven’t seen before. In most cases, the root object will be a dict or a list .
If you’ve pulled JSON data in from another program or have otherwise obtained a string of JSON formatted data in Python, you can easily deserialize that with loads() , which naturally loads from a string:
Voilà! You’ve tamed the wild JSON, and now it’s under your control. But what you do with that power is up to you. You could feed it, nurture it, and even teach it tricks. It’s not that I don’t trust you…but keep it on a leash, okay?
A Real World Example (sort of)
For your introductory example, you’ll use JSONPlaceholder, a great source of fake JSON data for practice purposes.
First create a script file called scratch.py , or whatever you want. I can’t really stop you.
You’ll need to make an API request to the JSONPlaceholder service, so just use the requests package to do the heavy lifting. Add these imports at the top of your file:
Now, you’re going to be working with a list of TODOs cuz like…you know, it’s a rite of passage or whatever.
Go ahead and make a request to the JSONPlaceholder API for the /todos endpoint. If you’re unfamiliar with requests , there’s actually a handy json() method that will do all of the work for you, but you can practice using the json library to deserialize the text attribute of the response object. It should look something like this:
You don’t believe this works? Fine, run the file in interactive mode and test it for yourself. While you’re at it, check the type of todos . If you’re feeling adventurous, take a peek at the first 10 or so items in the list.
See, I wouldn’t lie to you, but I’m glad you’re a skeptic.
What’s interactive mode? Ah, I thought you’d never ask! You know how you’re always jumping back and forth between the your editor and the terminal? Well, us sneaky Pythoneers use the -i interactive flag when we run the script. This is a great little trick for testing code because it runs the script and then opens up an interactive command prompt with access to all the data from the script!
All right, time for some action. You can see the structure of the data by visiting the endpoint in a browser, but here’s a sample TODO:
There are multiple users, each with a unique userId , and each task has a Boolean completed property. Can you determine which users have completed the most tasks?
Yeah, yeah, your implementation is better, but the point is, you can now manipulate the JSON data as a normal Python object!
I don’t know about you, but when I run the script interactively again, I get the following results:
That’s cool and all, but you’re here to learn about JSON. For your final task, you’ll create a JSON file that contains the completed TODOs for each of the users who completed the maximum number of TODOs.
All you need to do is filter todos and write the resulting list to a file. For the sake of originality, you can call the output file filtered_data_file.json . There are many ways you could go about this, but here’s one:
Perfect, you’ve gotten rid of all the data you don’t need and saved the good stuff to a brand new file! Run the script again and check out filtered_data_file.json to verify everything worked. It’ll be in the same directory as scratch.py when you run it.
Now that you’ve made it this far, I bet you’re feeling like some pretty hot stuff, right? Don’t get cocky: humility is a virtue. I am inclined to agree with you though. So far, it’s been smooth sailing, but you might want to batten down the hatches for this last leg of the journey.
Encoding and Decoding Custom Python Objects
What happens when we try to serialize the Elf class from that Dungeons & Dragons app you’re working on?
Not so surprisingly, Python complains that Elf isn’t serializable (which you’d know if you’ve ever tried to tell an Elf otherwise):
Although the json module can handle most built-in Python types, it doesn’t understand how to encode customized data types by default. It’s like trying to fit a square peg in a round hole—you need a buzzsaw and parental supervision.
Simplifying Data Structures
Now, the question is how to deal with more complex data structures. Well, you could try to encode and decode the JSON by hand, but there’s a slightly more clever solution that’ll save you some work. Instead of going straight from the custom data type to JSON, you can throw in an intermediary step.
All you need to do is represent your data in terms of the built-in types json already understands. Essentially, you translate the more complex object into a simpler representation, which the json module then translates into JSON. It’s like the transitive property in mathematics: if A = B and B = C, then A = C.
To get the hang of this, you’ll need a complex object to play with. You could use any custom class you like, but Python has a built-in type called complex for representing complex numbers, and it isn’t serializable by default. So, for the sake of these examples, your complex object is going to be a complex object. Confused yet?
Where do complex numbers come from? You see, when a real number and an imaginary number love each other very much, they add together to produce a number which is (justifiably) called complex.
A good question to ask yourself when working with custom types is What is the minimum amount of information necessary to recreate this object? In the case of complex numbers, you only need to know the real and imaginary parts, both of which you can access as attributes on the complex object:
Passing the same numbers into a complex constructor is enough to satisfy the __eq__ comparison operator:
Breaking custom data types down into their essential components is critical to both the serialization and deserialization processes.
Encoding Custom Types
To translate a custom object into JSON, all you need to do is provide an encoding function to the dump() method’s default parameter. The json module will call this function on any objects that aren’t natively serializable. Here’s a simple decoding function you can use for practice:
Notice that you’re expected to raise a TypeError if you don’t get the kind of object you were expecting. This way, you avoid accidentally serializing any Elves. Now you can try encoding complex objects for yourself!
Why did we encode the complex number as a tuple ? Great question! That certainly wasn’t the only choice, nor is it necessarily the best choice. In fact, this wouldn’t be a very good representation if you ever wanted to decode the object later, as you’ll see shortly.
The other common approach is to subclass the standard JSONEncoder and override its default() method:
Instead of raising the TypeError yourself, you can simply let the base class handle it. You can use this either directly in the dump() method via the cls parameter or by creating an instance of the encoder and calling its encode() method:
Decoding Custom Types
While the real and imaginary parts of a complex number are absolutely necessary, they are actually not quite sufficient to recreate the object. This is what happens when you try encoding a complex number with the ComplexEncoder and then decoding the result:
All you get back is a list, and you’d have to pass the values into a complex constructor if you wanted that complex object again. Recall our discussion about teleportation. What’s missing is metadata, or information about the type of data you’re encoding.
I suppose the question you really ought ask yourself is What is the minimum amount of information that is both necessary and sufficient to recreate this object?
The json module expects all custom types to be expressed as objects in the JSON standard. For variety, you can create a JSON file this time called complex_data.json and add the following object representing a complex number:
See the clever bit? That «__complex__» key is the metadata we just talked about. It doesn’t really matter what the associated value is. To get this little hack to work, all you need to do is verify that the key exists:
If «__complex__» isn’t in the dictionary, you can just return the object and let the default decoder deal with it.
Every time the load() method attempts to parse an object , you are given the opportunity to intercede before the default decoder has its way with the data. You can do this by passing your decoding function to the object_hook parameter.
Now play the same kind of game as before:
While object_hook might feel like the counterpart to the dump() method’s default parameter, the analogy really begins and ends there.
This doesn’t just work with one object either. Try putting this list of complex numbers into complex_data.json and running the script again:
If all goes well, you’ll get a list of complex objects:
You could also try subclassing JSONDecoder and overriding object_hook , but it’s better to stick with the lightweight solution whenever possible.
All done!
Congratulations, you can now wield the mighty power of JSON for any and all of your nefarious Python needs.
While the examples you’ve worked with here are certainly contrived and overly simplistic, they illustrate a workflow you can apply to more general tasks:
-
the json package.
- Read the data with load() or loads() .
- Process the data.
- Write the altered data with dump() or dumps() .
What you do with your data once it’s been loaded into memory will depend on your use case. Generally, your goal will be gathering data from a source, extracting useful information, and passing that information along or keeping a record of it.
Today you took a journey: you captured and tamed some wild JSON, and you made it back in time for supper! As an added bonus, learning the json package will make learning pickle and marshal a snap.
Good luck with all of your future Pythonic endeavors!
Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Working With JSON Data in Python
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About Lucas Lofaro
Lucas is a wandering Pythoneer with a curious mind and a desire to spread knowledge to those who seek it.
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