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This article mainly explains "how to understand the encoding and decoding method of Python3 built-in json module". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn "how to understand the encoding and decoding methods of Python3's built-in json module".

Catalogue

Introduction to JSON

Dumps coding

Coding dictionary

Coding list

Encoding string

Formatted output JSON

Conversion relation comparison table

Loads decoding

Summary

Introduction to JSON

JSON (JavaScript Object Notation) is a lightweight data exchange format based on a subset of ECMAScript. JSON adopts a completely language-independent text format, which makes JSON an ideal data exchange format, easy for people to read and write, and easy for machine parsing and generation, so it is not often used in interface data development and transmission.

In Python3, we use the built-in module json to decode and encode JSON objects. The json module provides four functions: dumps, dump, loads, and load

Dumps converts data types to strings

Dump converts the data type to a string and stores it in a file

Loads converts strings to data types

Load converts file opening from a string to a data type

Dumps coding

We use dumps to encode Python objects into JSON objects. Of course, dumps only serializes to str, while dump must pass a file descriptor to save the serialized str to the file.

Coding dictionary import json odata = {'www': 1,' pythontab.com': 2, 'Python3': 3} jdata = json.dumps (odata) print (jdata)

Example result:

{"www": 1, "pythontab.com": 2, "Python3": 3} Encoding list import json ldata = [100,' Python2', {'www': 1,' pythontab.com': 2, 'Python3': 3}] jdata = json.dumps (ldata) print (jdata)

Example result:

[100,100 "Python3", {"www": 1, "pythontab.com": 2, "Python3": 3}] Encoding string import json sdata = 'Python3'jdata = json.dumps (sdata) print (jdata)

Example result:

"Python3" formatted output JSON

Convert the following array to the standard json format

Import json ldata = ['Python3', 100,{' www': 1, 'pythontab.com': 2,' Python3': 3}, True] jdata = json.dumps (ldata, sort_keys=True, indent=4) print (jdata)

Example result:

["Python3", 100,100,{ "Python3": 3, "pythontab.com": 2, "www": 1}, true]

Parameter resolution:

Sort_keys=True, then the output of the dictionary is sorted in the order of keys.

Indent=4 represents an indentation of 4, and if indent is a non-negative integer or string, then JSON array elements and object members are beautified to the indentation level specified by that value.

Conversion relation comparison table

The following is a comparison table of the conversion of Python primitive types to JSON objects:

PythonJSONdictobjectlist, tuplearraystr, unicodestringint, long, floatnumberTruetrueFalsefalseNonenullloads decoding

We use loads to decode JSON objects. The decoding result is the corresponding Python object type. Of course, loads only completes deserialization, load only receives file descriptors, reads files and deserializes them.

For example, we use it to decode the data from the previous example.

Import json jsondata =''["Python3", 100,100,{ "Python3": 3, "pythontab.com": 2, "www": 1}, true] 'ldata = json.loads (jsondata) print (type (ldata)) print (ldata)

Example result:

['Python3', 100,{' Python3': 3, 'pythontab.com': 2,' www': 1}, True]

You can see that we have successfully decoded the JSON object in the previous example, and the final decoding result is the Python list object type, which is consistent with the result of the Python object JSON object comparison table.

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