Python常用系统自带库之json解析

摘要

本文介绍了Python系统类库json的几种用法，包含解析、转换、读取、写入、过滤，让我们看代码。

前提条件

导入类库

javascript 复制代码

import json
from datetime import datetime

设置文件编码utf-8

代码示例

1. 打开 JSON 文件

kotlin 复制代码

try:
    with open('data1.json', 'r', encoding='utf-8') as f:
        data = json.load(f)
    print(data) # 解析后的数据，输出: {'name': 'John', 'age': 30, 'city': 'New York', 'hobbies': ['reading', 'traveling', 'gaming']}
    print(type(data))  # 数据类型，输出: <class 'dict'>
except FileNotFoundError:
    print("错误：文件未找到，请检查路径是否正确。")
except json.JSONDecodeError as e:
    print(f"JSON 解析错误：{e}")
except Exception as e:
    print(f"未知错误：{e}")

2.将 Python 字典或列表转换回 JSON 字符串

sql 复制代码

dict_data = {'name': 'John', 'age': 30, 'city': 'New York', 'hobbies': ['reading', 'traveling', 'gaming']} #字典
list_data = ["reading", "cycling", "hiking"] #列表
#格式化打印
"""输出: 
{
    "age": 30,
    "city": "New York",
    "hobbies": [
        "reading",
        "traveling",
        "gaming"
    ],
    "name": "John"
}
[
    "reading",
    "cycling",
    "hiking"
]
"""
print(json.dumps(dict_data, ensure_ascii=False, sort_keys=True, indent=4))
print(json.dumps(list_data, ensure_ascii=False, sort_keys=True, indent=4))
#压缩打印
#输出: {"age": 30, "city": "New York", "hobbies": ["reading", "traveling", "gaming"], "name": "John"}
#输出: ["reading", "cycling", "hiking"]
print(json.dumps(dict_data, ensure_ascii=False, sort_keys=True))
print(json.dumps(list_data, ensure_ascii=False, sort_keys=True))

3.写入 JSON 数据到新文件

python 复制代码

with open('data2.json', 'w', encoding='utf-8') as f:
    json.dump(data, f, ensure_ascii=False, sort_keys=True, indent=4)

4.JSON逐行写入文件

python 复制代码

data_list = ['早上','中午','下午']
with open('data3.txt', 'w', encoding='utf-8') as f:
    for item in data_list:
        f.write(json.dumps(item, ensure_ascii=False) + '\n')
# 读取 JSON Lines 文件
with open('data3.txt', 'r', encoding='utf-8') as f:
    for line in f:
        item = json.loads(line)
        print(item)
"""输出: 
早上
中午
下午
"""

5.处理嵌套数据

python 复制代码

json_str = {
    "name": "Alice",
    "age": "30",
    "hobbies": ["reading", "null", "travel"],
    "address": {"city": "New York", "zip": "null"},
    "friends": [{"name": "Bob", "status": "active"}],
    "metadata": {"created_at": "2023-01-01T12:00:00", "tags": ["tech", "null", "life"]}
}

def process_nested(data):
    if isinstance(data, dict):
        for key, value in data.items():
            data[key] = process_nested(value)
    elif isinstance(data, list):
        for i, item in enumerate(data):
            data[i] = process_nested(item)
    elif isinstance(data, str):
        # 示例：将字符串 "null" 替换为 None
        if data.lower() == "null":
            return None
        # 将字符串形式的数字转换为整数或浮点数
        try:
            return int(data)
        except ValueError:
            try:
                return float(data)
            except ValueError:
                pass 
        try:
            dt = datetime.fromisoformat(data)
            return dt.date().isoformat()
        except ValueError:
            pass
    return data

#JSON中，空值必须用null表示（小写），而不是其他语言中的None、nil或undefined
#Python的json模块遵循这一规范，因此在序列化时会将Python的None转换为JSON的 null
cleaned_data = process_nested(json_str)
with open('data4.json', 'w', encoding='utf-8') as f:
    json.dump(cleaned_data, f, ensure_ascii=False, sort_keys=True, indent=4)

6.过滤 JSON 数据

css 复制代码

json_data = {
    "name": "Alice",
    "age": "30",
    "hobbies": ["reading", "null", "travel"],
    "address": {"city": "New York", "zip": "null"},
    "friends": [{"name": "Bob", "status": "active"}],
    "metadata": {"created_at": "2023-01-01T12:00:00", "tags": ["tech", "null", "life"]}
}
filtered_data = {key: value for key, value in json_data.items() if key in ['name', 'age']}
print(filtered_data)  # 输出: {'name': 'Alice', 'age': '30'}

结果展示

总结

以上我们了解了Python系统类库json的几种用法，包含解析、转换、读取、写入、过滤，环绕dumps、dump、loads、load这几个方法展开。

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