Use of Json module and Pickle module in Python
Serializing and deserializing data are common data operations. Python provides two modules to facilitate developers to implement data serialization operations, namely json modules and pickle modules. The main differences between these two modules are as follows:
json is a text serialization format, while pickle is a binary serialization format;
json can be read intuitively, but pickle cannot;
json is interoperable and widely used outside the Python system, while pickle is specific to Python;
By default, json can only represent a subset of Python's built-in types. It cannot represent custom classes;
but pickle can represent a large number of Python data types.
Recommended learning: Python video tutorial
Json module
Json is a lightweight data exchange format. Due to its characteristics of small amount of transmitted data and easy parsing of data format, it is widely used in interactive operations between various systems. As a kind of data format to pass data. It contains multiple commonly used functions, as follows:
dumps() function
dumps() function can encode Python objects into Json strings. For example:
# 字典转成json字符串 加上ensure_ascii = False以后, 可以识别中文, indent = 4 是间隔4个空格显示 import json d = { '小明': { 'sex': '男', 'addr': '上海', 'age': 26 }, '小红': { 'sex': '女', 'addr': '上海', 'age': 24 }, } print(json.dumps(d, ensure_ascii = False, indent = 4)) # 执行结果: { "小明": { "sex": "男", "addr": "上海", "age": 26 }, "小红": { "sex": "女", "addr": "上海", "age": 24 } }
dump() function
dump() function can encode Python objects into json strings and automatically write them to files. No need to Write files separately. For example:
# 字典转成json字符串, 不需要写文件, 自动转成的json字符串写入到‘ users.json’ 的文件中 import json d = { '小明': { 'sex': '男', 'addr': '上海', 'age': 26 }, '小红': { 'sex': '女', 'addr': '上海', 'age': 24 }, }# 打开一个名字为‘ users.json’ 的空文件 fw = open('users.json', 'w', encoding = 'utf-8') json.dump(d, fw, ensure_ascii = False, indent = 4)
loads() function
loads() function can convert a json string into a Python data type. For example:
# 这是users.json文件中的内容 { "小明": { "sex": "男", "addr": "上海", "age": 26 }, "小红": { "sex": "女", "addr": "上海", "age": 24 } } #!/usr/bin / python3# 把json串变成python的数据类型 import json# 打开‘ users.json’ 的json文件 f = open('users.json', 'r', encoding = 'utf-8')# 读文件 res = f.read() print(json.loads(res)) # 执行结果: { '小明': { 'sex': '男', 'addr': '上海', 'age': 26 }, '小红': { 'sex': '女', 'addr': '上海', 'age': 24 } }
load() function
load() has a similar function to loads(). The load() function can convert a json string into a Python data type. The difference is that the parameter of the former is a file object, and there is no need to read this file separately. For example:
# 把json串变成python的数据类型: 字典, 传一个文件对象, 不需要再单独读文件 import json# 打开文件 f = open('users.json', 'r', encoding = 'utf-8') print(json.load(f)) # 执行结果: { '小明': { 'sex': '男', 'addr': '上海', 'age': 26 }, '小红': { 'sex': '女', 'addr': '上海', 'age': 24 } }
Pickle module
The Pickle module has similar functions to the Json module and also contains four functions, namely dump(), dumps(), loads() and load(), their main differences are as follows:
The difference between dumps and dump is that the former serializes the object, while the latter serializes the object and saves it to a file. The difference between loads and load is that the former deserializes the serialized string, while the latter reads the serialized string from the file and deserializes it.
dumps() function
dumps() function can convert data in a special form into a string that is only recognized by the python language, for example:
import pickle# dumps功能 import pickle data = ['A', 'B', 'C', 'D'] print(pickle.dumps(data)) b '\x80\x03]q\x00(X\x01\x00\x00\x00Aq\x01X\x01\x00\x00\x00Bq\x02X\x01\x00\x00\x00Cq\x03X\x01\x00\x00\x00Dq\x04e.'
dump() function
The dump() function can convert data into a string that is only recognized by the python language in a special form and write it to a file. For example:
# dump功能 with open('test.txt', 'wb') as f: pickle.dump(data, f) print('写入成功')
Write successfully
loads() function
loads() function can convert pickle data into python data structure. For example:
# loads功能 msg = pickle.loads(datastr) print(msg) ['A', 'B', 'C', 'D']
load() function
The load() function can read data from a data file and convert it into a python data structure. For example:
# load功能with open('test.txt', 'rb') as f: data = pickle.load(f) print(data) ['A', 'B', 'C', 'D']
This article comes from the python tutorial column, welcome to learn!
The above is the detailed content of Use of Json module and Pickle module in Python. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



The combination of golangWebSocket and JSON: realizing data transmission and parsing In modern Web development, real-time data transmission is becoming more and more important. WebSocket is a protocol used to achieve two-way communication. Unlike the traditional HTTP request-response model, WebSocket allows the server to actively push data to the client. JSON (JavaScriptObjectNotation) is a lightweight format for data exchange that is concise and easy to read.

The Gson@Expose annotation can be used to mark whether a field is exposed (contained or not) for serialization or deserialization. The @Expose annotation can take two parameters, each parameter is a boolean value and can take the value true or false. In order for GSON to react to the @Expose annotation, we have to create a Gson instance using the GsonBuilder class and need to call the excludeFieldsWithoutExposeAnnotation() method, which configures Gson to exclude all fields without Expose annotation from serialization or deserialization. Syntax publicGsonBuilderexclud

MySQL5.7 and MySQL8.0 are two different MySQL database versions. There are some main differences between them: Performance improvements: MySQL8.0 has some performance improvements compared to MySQL5.7. These include better query optimizers, more efficient query execution plan generation, better indexing algorithms and parallel queries, etc. These improvements can improve query performance and overall system performance. JSON support: MySQL 8.0 introduces native support for JSON data type, including storage, query and indexing of JSON data. This makes processing and manipulating JSON data in MySQL more convenient and efficient. Transaction features: MySQL8.0 introduces some new transaction features, such as atomic

Performance optimization methods for converting PHP arrays to JSON include: using JSON extensions and the json_encode() function; adding the JSON_UNESCAPED_UNICODE option to avoid character escaping; using buffers to improve loop encoding performance; caching JSON encoding results; and considering using a third-party JSON encoding library.

How to handle XML and JSON data formats in C# development requires specific code examples. In modern software development, XML and JSON are two widely used data formats. XML (Extensible Markup Language) is a markup language used to store and transmit data, while JSON (JavaScript Object Notation) is a lightweight data exchange format. In C# development, we often need to process and operate XML and JSON data. This article will focus on how to use C# to process these two data formats, and attach

Use the json.MarshalIndent function in golang to convert the structure into a formatted JSON string. When writing programs in Golang, we often need to convert the structure into a JSON string. In this process, the json.MarshalIndent function can help us. Implement formatted output. Below we will explain in detail how to use this function and provide specific code examples. First, let's create a structure containing some data. The following is an indication

Quick Start: Pandas method of reading JSON files, specific code examples are required Introduction: In the field of data analysis and data science, Pandas is one of the important Python libraries. It provides rich functions and flexible data structures, and can easily process and analyze various data. In practical applications, we often encounter situations where we need to read JSON files. This article will introduce how to use Pandas to read JSON files, and attach specific code examples. 1. Installation of Pandas

Annotations in the Jackson library control JSON serialization and deserialization: Serialization: @JsonIgnore: Ignore the property @JsonProperty: Specify the name @JsonGetter: Use the get method @JsonSetter: Use the set method Deserialization: @JsonIgnoreProperties: Ignore the property @ JsonProperty: Specify name @JsonCreator: Use constructor @JsonDeserialize: Custom logic
