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Python中json.loads和json.dumps方法中英雙語詳解

 更新時(shí)間：2025年01月26日 10:13:14   作者：阿正的夢(mèng)工坊  
在Python中json.loads和json.dumps是處理JSON數(shù)據(jù)的重要方法,json.loads用于將JSON字符串解析為Python對(duì)象,而json.dumps用于將Python對(duì)象序列化為JSON字符串,文中通過代碼介紹的非常詳細(xì),需要的朋友可以參考下

前言

在 Python 的標(biāo)準(zhǔn)庫中，json 模塊用于處理 JSON 數(shù)據(jù)格式。JSON（JavaScript Object Notation）是一種輕量級(jí)的數(shù)據(jù)交換格式，廣泛用于前后端交互以及數(shù)據(jù)存儲(chǔ)。json.loads 和 json.dumps 是 json 模塊中最常用的兩個(gè)方法，分別用于解析 JSON 字符串和將 Python 對(duì)象序列化為 JSON 字符串。

1. json.loads 方法

功能

json.loads 用于將 JSON 格式的字符串解析為 Python 數(shù)據(jù)結(jié)構(gòu)（如字典、列表等）。

語法

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

主要參數(shù)

  • s：要解析的 JSON 字符串。
  • encoding（Python 3 中已棄用）：指定字符編碼。
  • cls：自定義解碼類，默認(rèn)為 None。
  • object_hook：一個(gè)可選函數(shù)，允許自定義將 JSON 對(duì)象轉(zhuǎn)換為其他類型的 Python 對(duì)象。
  • parse_float 和 parse_int：自定義將 JSON 中的浮點(diǎn)數(shù)和整數(shù)轉(zhuǎn)換為特定類型。
  • object_pairs_hook：用于處理 JSON 對(duì)象中的鍵值對(duì)，默認(rèn)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 將 JSON 字符串解析為 Python 字典
data = json.loads(json_str)
print(data)
# 輸出：{'name': 'Alice', 'age': 25, 'skills': ['Python', 'Machine Learning']}

# 訪問解析后的數(shù)據(jù)
print(data["name"])  # 輸出：Alice
print(data["skills"])  # 輸出：['Python', 'Machine Learning']

2. json.dumps 方法

功能

json.dumps 用于將 Python 對(duì)象序列化為 JSON 格式的字符串。

語法

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)

主要參數(shù)

  • obj：需要序列化為 JSON 的 Python 對(duì)象。
  • skipkeys：是否跳過非字符串類型的鍵，默認(rèn)為 False。
  • ensure_ascii：默認(rèn) True，確保 JSON 中的所有非 ASCII 字符被轉(zhuǎn)義。如果為 False，將輸出原始 Unicode 字符。
  • indent：格式化輸出的縮進(jìn)級(jí)別。如果為 None，輸出緊湊的單行 JSON；如果設(shè)置為數(shù)字（如 4），輸出帶縮進(jìn)的多行 JSON。
  • separators：自定義鍵值對(duì)之間和項(xiàng)之間的分隔符。
  • sort_keys：是否對(duì)字典的鍵進(jìn)行排序，默認(rèn)為 False。

示例

import json

# 示例 Python 對(duì)象
data = {
    "name": "Bob",
    "age": 30,
    "skills": ["JavaScript", "React", "Node.js"],
    "is_active": True
}

# 使用 json.dumps 將 Python 對(duì)象序列化為 JSON 字符串
json_str = json.dumps(data)
print(json_str)
# 輸出：{"name": "Bob", "age": 30, "skills": ["JavaScript", "React", "Node.js"], "is_active": true}

# 格式化輸出（帶縮進(jìn)）
json_str_pretty = json.dumps(data, indent=4, ensure_ascii=False)
print(json_str_pretty)
# 輸出：
# {
#     "name": "Bob",
#     "age": 30,
#     "skills": [
#         "JavaScript",
#         "React",
#         "Node.js"
#     ],
#     "is_active": true
# }

3. json.loads 和 json.dumps 的結(jié)合使用

在實(shí)際應(yīng)用中，json.loads 和 json.dumps 常常配合使用。例如，我們可能需要先從文件或網(wǎng)絡(luò)中讀取 JSON 數(shù)據(jù)，將其解析為 Python 對(duì)象進(jìn)行處理，然后再將處理后的結(jié)果保存為 JSON 格式。

示例

import json

# 示例 JSON 字符串
json_str = '{"name": "Eve", "age": 28, "hobbies": ["Reading", "Swimming"]}'

# 將 JSON 字符串解析為 Python 字典
data = json.loads(json_str)
print("解析后的數(shù)據(jù)：", data)

# 修改數(shù)據(jù)
data["age"] = 29
data["hobbies"].append("Hiking")

# 將修改后的數(shù)據(jù)序列化為 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)

輸出結(jié)果：

解析后的數(shù)據(jù)： {'name': 'Eve', 'age': 28, 'hobbies': ['Reading', 'Swimming']}
修改后的 JSON：
{
    "name": "Eve",
    "age": 29,
    "hobbies": [
        "Reading",
        "Swimming",
        "Hiking"
    ]
}

4. 常見錯(cuò)誤及解決辦法

1）解析無效的 JSON 字符串

如果輸入的字符串不是有效的 JSON 格式，json.loads 會(huì)拋出 JSONDecodeError 異常。

import json

invalid_json = "{'name': 'Alice', 'age': 25}"  # 錯(cuò)誤的 JSON 格式（單引號(hào)）
try:
    data = json.loads(invalid_json)
except json.JSONDecodeError as e:
    print(f"JSONDecodeError: {e}")

解決辦法：確保 JSON 字符串使用雙引號(hào)表示字符串內(nèi)容。

2）非 JSON 可序列化的對(duì)象

如果 json.dumps 的輸入對(duì)象中包含非 JSON 支持的數(shù)據(jù)類型（如 datetime），會(huì)拋出 TypeError。

import json
from datetime import datetime

data = {"name": "Alice", "timestamp": datetime.now()}

try:
    json_str = json.dumps(data)
except TypeError as e:
    print(f"TypeError: {e}")

解決辦法：使用 default 參數(shù)自定義序列化方式。

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

5. 總結(jié)

  • json.loads：將 JSON 字符串解析為 Python 數(shù)據(jù)結(jié)構(gòu)。
  • json.dumps：將 Python 數(shù)據(jù)結(jié)構(gòu)序列化為 JSON 字符串。
  • 配合使用可以實(shí)現(xiàn) JSON 數(shù)據(jù)的讀取、處理和保存。
  • 在處理中文或特殊字符時(shí)，可以通過設(shè)置 ensure_ascii=False 保留原始字符。

通過合理使用 json 模塊的方法，我們可以輕松地在 Python 中操作 JSON 數(shù)據(jù)，滿足數(shù)據(jù)交換和存儲(chǔ)的需求。

英文版

Detailed Explanation of Python’s json.loads and json.dumps Methods

In Python’s standard library, the json module 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 the json module are json.loads and json.dumps, which are used for parsing JSON strings and serializing Python objects to JSON strings, respectively.

1. json.loads Method

Function

json.loads is 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 to None.
  • object_hook: An optional function that allows custom conversion of JSON objects into other Python objects.
  • parse_float and parse_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 string
json_str = '{"name": "Alice", "age": 25, "skills": ["Python", "Machine Learning"]}'

# Using json.loads to parse the JSON string into a Python dictionary
data = json.loads(json_str)
print(data)
# Output: {'name': 'Alice', 'age': 25, 'skills': ['Python', 'Machine Learning']}

# Accessing parsed data
print(data["name"])  # Output: Alice
print(data["skills"])  # Output: ['Python', 'Machine Learning']

2. json.dumps Method

Function

json.dumps is 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 to False).
  • ensure_ascii: Default True, ensures all non-ASCII characters in the JSON are escaped. If set to False, original Unicode characters will be output.
  • indent: The number of spaces to use for pretty-printing the JSON. If None, 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 is False.

Example

import json

# Sample Python object
data = {
    "name": "Bob",
    "age": 30,
    "skills": ["JavaScript", "React", "Node.js"],
    "is_active": True
}

# Using json.dumps to serialize the Python object to a JSON string
json_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 indent
json_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. Using json.loads and json.dumps Together

In real-world applications, json.loads and json.dumps are 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 string
json_str = '{"name": "Eve", "age": 28, "hobbies": ["Reading", "Swimming"]}'

# Parsing the JSON string into a Python dictionary
data = json.loads(json_str)
print("Parsed data:", data)

# Modifying the data
data["age"] = 29
data["hobbies"].append("Hiking")

# Serializing the modified data back to JSON
new_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.loads will raise a JSONDecodeError exception.

import json

invalid_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 to json.dumps contains non-JSON serializable types (like datetime), it will raise a TypeError.

import json
from datetime import datetime

data = {"name": "Alice", "timestamp": datetime.now()}

try:
    json_str = json.dumps(data)
except TypeError as e:
    print(f"TypeError: {e}")

Solution: Use the default parameter 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, set ensure_ascii=False.

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

例子：讀取JSON文件前兩個(gè)數(shù)據(jù)

import json

# 指定文件路徑
file_path = "/code/peft_study/open-instruct/data/tulu-3-sft-mixture-json-sampled/train_sampled_9k.json"

# 讀取 JSON 文件并輸出前兩條數(shù)據(jù)
with open(file_path, "r") as file:
    for i, line in enumerate(file):
        if i < 2:  # 只輸出前兩條數(shù)據(jù)
            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"
}

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