Python中json.loads和json.dumps方法中英双语详解

目录

前言1.json.loads方法功能语法主要参数示例2.json.dumps方法功能语法主要参数示例3.json.loads和json.dumps的结合使用示例4. 常见错误及解决办法1）解析无效的 JSON 字符串2）非 JSON 可序列化的对象5. 总结英文版Detailed Explanation of Python’sjson.loadsandjson.dumpsMethods1.json.loadsMethodFunctionSyntaxMain ParametersExample2.json.dumpsMethodFunctionSyntaxMain ParametersExample3. Usingjson.loadsandjson.dumpsTogetherExample4. Common Errors and Solutions1) Invalid JSON String2) Non-JSON Serializable Objects5. Summary例子：读取JSON文件前两个数据总结

前言

在 Python 的标准库中，json模块用于处理 JSON 数据格式。JSON（JavaScript Object Notation）是一种轻量级的数据交换格式，广泛用于前后端交互以及数据存储。json.loads和json.dumps是json模块中最常用的两个方法，分别用于解析 JSON 字符串和将 Python 对象序列化为 JSON 字符串。

1.json.loads方法

功能

json.loads用于将 JSON 格式的字符串解析为 Python 数据结构（如字典、列表等）。

语法

json.loads(s, *, encoding=None, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)

主要参数

s：要解析的 JSON 字符串。encoding（Python 3 中已弃用）：指定字符编码。cls：自定义解码类，默认为None。object_hook：一个可选函数，允许自定义将 JSON 对象转换为其他类型的 Python 对象。parse_float和parse_int：自定义将 JSON 中的浮点数和整数转换为特定类型。object_pairs_hook：用于处理 JSON 对象中的键值对，默认返回字典。

示例

import json# 示例 JSON 字符串json_str = '{"name": "Alice", "age": 25, "skills": ["Python", "Machine Learning"]}'# 使用 json.loads 将 JSON 字符串解析为 Python 字典data = json.loads(json_str)print(data)# 输出：{'name': 'Alice', 'age': 25, 'skills': ['Python', 'Machine Learning']}# 访问解析后的数据print(data["name"])  # 输出：Aliceprint(data["skills"])  # 输出：['Python', 'Machine Learning']

2.json.dumps方法

功能

json.dumps用于将 Python 对象序列化为 JSON 格式的字符串。

语法

json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)

主要参数

obj：需要序列化为 JSON 的 Python 对象。skipkeys：是否跳过非字符串类型的键，默认为False。ensure_ascii：默认True，确保 JSON 中的所有非 ASCII 字符被转义。如果为False，将输出原始 Unicode 字符。indent：格式化输出的缩进级别。如果为None，输出紧凑的单行 JSON；如果设置为数字（如4），输出带缩进的多行 JSON。separators：自定义键值对之间和项之间的分隔符。sort_keys：是否对字典的键进行排序，默认为False。

示例

import json# 示例 Python 对象data = {    "name": "Bob",    "age": 30,    "skills": ["JavaScript", "React", "Node.js"],    "is_active": True}# 使用 json.dumps 将 Python 对象序列化为 JSON 字符串json_str = json.dumps(data)print(json_str)# 输出：{"name": "Bob", "age": 30, "skills": ["JavaScript", "React", "Node.js"], "is_active": true}# 格式化输出（带缩进）json_str_pretty = json.dumps(data, indent=4, ensure_ascii=False)print(json_str_pretty)# 输出：# {#     "name": "Bob",#     "age": 30,#     "skills": [#         "JavaScript",#         "React",#         "Node.js"#     ],#     "is_active": true# }

3.json.loads和json.dumps的结合使用

在实际应用中，json.loads和json.dumps常常配合使用。例如，我们可能需要先从文件或网络中读取 JSON 数据，将其解析为 Python 对象进行处理，然后再将处理后的结果保存为 JSON 格式。

示例

import json# 示例 JSON 字符串json_str = '{"name": "Eve", "age": 28, "hobbies": ["Reading", "Swimming"]}'# 将 JSON 字符串解析为 Python 字典data = json.loads(json_str)print("解析后的数据：", data)# 修改数据data["age"] = 29data["hobbies"].append("Hiking")# 将修改后的数据序列化为 JSON 字符串new_json_str = json.dumps(data, indent=4, ensure_ascii=False)print("修改后的 JSON：\n", new_json_str)

输出结果：

解析后的数据： {'name': 'Eve', 'age': 28, 'hobbies': ['Reading', 'Swimming']}修改后的 JSON：{    "name": "Eve",    "age": 29,    "hobbies": [        "Reading",        "Swimming",        "Hiking"    ]}

4. 常见错误及解决办法

1）解析无效的 JSON 字符串

如果输入的字符串不是有效的 JSON 格式，json.loads会抛出JSONDecodeError异常。

import jsoninvalid_json = "{'name': 'Alice', 'age': 25}"  # 错误的 JSON 格式（单引号）try:    data = json.loads(invalid_json)except json.JSONDecodeError as e:    print(f"JSONDecodeError: {e}")

解决办法：确保 JSON 字符串使用双引号表示字符串内容。

2）非 JSON 可序列化的对象

如果json.dumps的输入对象中包含非 JSON 支持的数据类型（如datetime），会抛出TypeError。

import jsonfrom datetime import datetimedata = {"name": "Alice", "timestamp": datetime.now()}try:    json_str = json.dumps(data)except TypeError as e:    print(f"TypeError: {e}")

解决办法：使用default参数自定义序列化方式。

json_str = json.dumps(data, default=str)print(json_str)# 输出：{"name": "Alice", "timestamp": "2024-12-24 15:30:00.123456"}

5. 总结

json.loads：将 JSON 字符串解析为 Python 数据结构。json.dumps：将 Python 数据结构序列化为 JSON 字符串。配合使用可以实现 JSON 数据的读取、处理和保存。在处理中文或特殊字符时，可以通过设置ensure_ascii=False保留原始字符。

通过合理使用json模块的方法，我们可以轻松地在 Python 中操作 JSON 数据，满足数据交换和存储的需求。

英文版

Detailed Explanation of Python’sjson.loadsandjson.dumpsMethods

In Python’s standard library, thejsonmodule is used for handling JSON data format. JSON (JavaScript Object Notation) is a lightweight data exchange format that is widely used in both front-end and back-end communication as well as data storage. Among the most commonly used methods in thejsonmodule arejson.loadsandjson.dumps, which are used forparsing JSON stringsandserializing Python objects to JSON strings, respectively.

1.json.loadsMethod

Function

json.loadsis used to parse a JSON-formatted string into a Python data structure (such as a dictionary, list, etc.).

Syntax

json.loads(s, *, encoding=None, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)

Main Parameters

s: The JSON string to be parsed.encoding(deprecated in Python 3): Specifies the character encoding.cls: Custom decoder class, defaults toNone.object_hook: An optional function that allows custom conversion of JSON objects into other Python objects.parse_floatandparse_int: Custom parsers for floating-point numbers and integers in JSON.object_pairs_hook: Used for handling key-value pairs in JSON objects, defaults to returning a dictionary.

Example

import json# Sample JSON stringjson_str = '{"name": "Alice", "age": 25, "skills": ["Python", "Machine Learning"]}'# Using json.loads to parse the JSON string into a Python dictionarydata = json.loads(json_str)print(data)# Output: {'name': 'Alice', 'age': 25, 'skills': ['Python', 'Machine Learning']}# Accessing parsed dataprint(data["name"])  # Output: Aliceprint(data["skills"])  # Output: ['Python', 'Machine Learning']

2.json.dumpsMethod

Function

json.dumpsis used to serialize a Python object into a JSON-formatted string.

Syntax

json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)

Main Parameters

obj: The Python object to be serialized into JSON.skipkeys: Whether to skip keys that are not strings (defaults toFalse).ensure_ascii: DefaultTrue, ensures all non-ASCII characters in the JSON are escaped. If set toFalse, original Unicode characters will be output.indent: The number of spaces to use for pretty-printing the JSON. IfNone, the output will be compact and on one line. If set to a number (e.g.,4), the output will be formatted with indents.separators: Custom separators for key-value pairs and items in JSON.sort_keys: Whether to sort the dictionary keys. Default isFalse.

Example

import json# Sample Python objectdata = {    "name": "Bob",    "age": 30,    "skills": ["JavaScript", "React", "Node.js"],    "is_active": True}# Using json.dumps to serialize the Python object to a JSON stringjson_str = json.dumps(data)print(json_str)# Output: {"name": "Bob", "age": 30, "skills": ["JavaScript", "React", "Node.js"], "is_active": true}# Pretty-printing the JSON with indentjson_str_pretty = json.dumps(data, indent=4, ensure_ascii=False)print(json_str_pretty)# Output:# {#     "name": "Bob",#     "age": 30,#     "skills": [#         "JavaScript",#         "React",#         "Node.js"#     ],#     "is_active": true# }

3. Usingjson.loadsandjson.dumpsTogether

In real-world applications,json.loadsandjson.dumpsare often used in tandem. For instance, you may need to first read JSON data from a file or network, parse it into a Python object for processing, and then serialize the processed result back into JSON format.

Example

import json# Sample JSON stringjson_str = '{"name": "Eve", "age": 28, "hobbies": ["Reading", "Swimming"]}'# Parsing the JSON string into a Python dictionarydata = json.loads(json_str)print("Parsed data:", data)# Modifying the datadata["age"] = 29data["hobbies"].append("Hiking")# Serializing the modified data back to JSONnew_json_str = json.dumps(data, indent=4, ensure_ascii=False)print("Modified JSON:\n", new_json_str)

Output:

Parsed data: {'name': 'Eve', 'age': 28, 'hobbies': ['Reading', 'Swimming']}Modified JSON:{    "name": "Eve",    "age": 29,    "hobbies": [        "Reading",        "Swimming",        "Hiking"    ]}

4. Common Errors and Solutions

1) Invalid JSON String

If the input string is not a valid JSON format,json.loadswill raise aJSONDecodeErrorexception.

import jsoninvalid_json = "{'name': 'Alice', 'age': 25}"  # Invalid JSON format (single quotes)try:    data = json.loads(invalid_json)except json.JSONDecodeError as e:    print(f"JSONDecodeError: {e}")

Solution: Ensure that JSON strings use double quotes for string content.

2) Non-JSON Serializable Objects

If the object passed tojson.dumpscontains non-JSON serializable types (likedatetime), it will raise aTypeError.

import jsonfrom datetime import datetimedata = {"name": "Alice", "timestamp": datetime.now()}try:    json_str = json.dumps(data)except TypeError as e:    print(f"TypeError: {e}")

Solution: Use thedefaultparameter to define custom serialization.

json_str = json.dumps(data, default=str)print(json_str)# Output: {"name": "Alice", "timestamp": "2024-12-24 15:30:00.123456"}

5. Summary

json.loads: Parses a JSON string into a Python data structure.json.dumps: Serializes a Python data structure into a JSON string.Using both methods together allows for reading, processing, and saving JSON data.To handle special characters or non-ASCII text, setensure_ascii=False.

By effectively using the methods in thejsonmodule, we can easily handle JSON data in Python, enabling seamless data exchange and storage.

例子：读取JSON文件前两个数据

import json# 指定文件路径file_path = "/code/peft_study/open-instruct/data/tulu-3-sft-mixture-json-sampled/train_sampled_9k.json"# 读取 JSON 文件并输出前两条数据with open(file_path, "r") as file:    for i, line in enumerate(file):        if i < 2:  # 只输出前两条数据            data = json.loads(line)            print(json.dumps(data, indent=4, ensure_ascii=False))        else:            break

Output

{    "id": "personahub_xdout465m7opc85m7bjfqmdt",    "messages": [        {            "content": "Write a python function to analyze a list of Bollywood movie titles and return a list of titles that are palindromes. A palindrome is a word, phrase, number, or other sequence of characters which reads the same backward as forward. For simplicity, you can ignore spaces, capitalization, and punctuation in the titles. \n\nInput:\n- A list of strings, where each string represents a Bollywood movie title.\n\nOutput:\n- A list of strings, where each string is a movie title from the input list that is a palindrome.\n\nExample:\n```python\nmovie_titles = [\"Dil Se\", \"Madam\", \"Racecar\", \"Raees\", \"Noon\"]\nprint(find_palindrome_titles(movie_titles))\n```\nExpected Output:\n```python\n[\"Madam\", \"Racecar\", \"Noon\"]\n```",            "role": "user"        },        {            "content": "def find_palindrome_titles(movie_titles):\n    palindrome_titles = []\n    for title in movie_titles:\n        cleaned_title = ''.join(char for char in title.lower() if char.isalnum())\n        if cleaned_title == cleaned_title[::-1]:\n            palindrome_titles.append(title)\n    return palindrome_titles",            "role": "assistant"        }    ],    "source": "ai2-adapt-dev/personahub_code_v2_34999"}{    "id": "ai2-adapt-dev/flan_v2_converted_33757",    "messages": [        {            "content": "In this task, you are given two phrases: Head and Tail, separated with <sep>. The Head and the Tail events are short phrases possibly involving participants. The names of specific people have been replaced by generic words (e.g., PersonX, PersonY, PersonZ). PersonX is always the subject of the event. You have to determine whether the Head can be characterized by being or having the Tail or not. Being characterized usually describes entities' general characteristics such as rose is red, or subjective attributes such as thirst is uncomfortable. It can also map to descriptors that speak to the substance or value of items such as meat has the property of being stored in the freezer or bike is powered by a person's legs. Classify your answers into \"Yes\" and \"No\". The phrase may also contain \"___\", a placeholder that can be an object, a person, and/or an action.\n\nLet me give you an example: Head: water<sep>Tail: effect of making things wet\nThe answer to this example can be: Yes\nHere is why: This is a good example. The water can be characterized by making things wet.\n\nOK. solve this:\nHead: PersonX always watch ___<sep>Tail: engaged\nAnswer:",            "role": "user"        },        {            "content": "No",            "role": "assistant"        }    ],    "source": "ai2-adapt-dev/flan_v2_converted"}

总结