The example in this article describes the encoding and decoding method of json format data in Python. Share it with everyone for your reference, the details are as follows:
Python has built-in processing methods for json data format starting from version 2.6.
1. json format data encoding
In python, json data format encoding uses the json.dumps method.
#!/usr/bin/env python #coding=utf8 import json users = [{'name': 'tom', 'age': 22}, {'name': 'anny', 'age': 18}] #元组对象也可以 #users = ({'name': 'tom', 'age': 22}, {'name': 'anny', 'age': 18}) #输出[{"age": 22, "name": "tom"}, {"age": 18, "name": "anny"}] print json.dumps(users)
where users can be a tuple object or a list object. The elements within the object can be numbers, strings, tuples, lists, None, and Boolean values.
#!/usr/bin/env python #coding=utf8 import json random = (5, [1, 2], "tom\" is good", (1, 2), 1.5, True, None) #输出[5, [1, 2], "tom\" is good", [1, 2], 1.5, true, null] print json.dumps(random)
2. Decoding json format data
In python, json format data decoding uses the json.loads method. Apply the above example:
#!/usr/bin/env python #coding=utf8 import json random = (5, [1, 2], "tom\" is good", (1, 2), 1.5, True, None) jsonObj = json.dumps(random) #输出[5, [1, 2], u'tom" is good', [1, 2], 1.5, True, None] print json.loads(jsonObj)
Here is to first encode a data json, and then decode the encoded data. Logically speaking, the decoded data should be the same as the original data, but we found that the tuple objects here have been replaced with list objects. This involves the definition of data formats for conversion between python and json. Look at the following two pictures:
Convert python to json data format definition
Convert json to python data format definition
It can be seen from the above two figures that when python is converted to json, list and tuple will be converted to array, but when json is converted to python, array will only be converted to list.
Note: The content of the above two pictures comes from the python official website. The dumps method and loads method of json also have other parameters that can be used.
For more articles related to encoding and decoding json format data in Python, please pay attention to the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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