python—json模块的编码与解码

文章目录

引言
1.json.dumps()与json.dump()
2.json.loads()与json.load()
3.json.dumps参数介绍
4.将JSON对象转换成DataFrame或者其他数据结构
5.总结

引言

JSON(JavaScript Object Notation)模块是一种轻量级的文本数据交换格式，可以理解为键值对的集合，JSON是存储和交换文本信息的语法。下面展示一个JSON实例。

{"id": 29738501,"name": "跟着你到天边 钢琴版","duration": 174001,"hearTime": 0,"commentThreadId": "R_SO_4_29738501","score": 40,  # 得分"mvid": 0,"hMusic": null,"disc": "","fee": 0,"no": 1,"rtUrl": null,"ringtone": null,"rtUrls": [],"rurl": null,"status": 0,"ftype": 0,"mp3Url": "http://m2.music.126.net/vrVa20wHs8iIe0G8Oe7I9Q==/3222668581877701.mp3","audition": null,"playedNum": 0,"copyrightId": 0,"rtype": 0,"crbt": null,"popularity": 40,    # 流行度"dayPlays": 0,"alias": [],"copyFrom": "","position": 1,"starred": false,   # 是否有被加星"starredNum": 0"artists": [{"img1v1Url": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg","name": "群星","briefDesc": "","albumSize": 0,"img1v1Id": 0,"musicSize": 0,"alias": [],"picId": 0,"picUrl": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg","trans": "","id": 122455}],"artist": {"img1v1Url": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg","name": "","briefDesc": "","albumSize": 0,"img1v1Id": 0,"musicSize": 0,"alias": [],"picId": 0,"picUrl": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg","trans": "","id": 0}}
}

python中可以使用json模块对JSON数据进行编码与解码，分别是dumps()、dump()、loads()函数、load()函数

1.json.dumps()与json.dump()

json.dumps()函数将python对象编码成JSON字符串。json.dump()函数将python对象编码成JSON字符串并写入到文件中。

import jsondata = [{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}]data2 = json.dumps(data)
print(data2)
print(type(data2))

[{"a": 1, "b": 2, "c": 3, "d": 4, "e": 5}]
<class 'str'>

import jsondata = [{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}]
with open('f.json', 'w') as f:data2 = json.dump(data, f)

会生成f.json文件

2.json.loads()与json.load()

json.loads()函数将已编码的JSON字符串解码成python对象。json.load()函数将JSON数据解码成python对象

import jsondata = [{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}]data2 = json.dumps(data)
print(type(data2))
data3 = json.loads(data2)
print(type(data3))

<class 'str'>
<class 'list'>

import jsonwith open('f.json','r') as f:print(type(f))data = json.load(f)print(data)print(type(data))

<class '_io.TextIOWrapper'>
[{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}]
<class 'list'>

3.json.dumps参数介绍

json.dumps(obj, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None,separators=None, encoding="utf-8", default=None, sort_keys=False, **kw)

详细介绍见该网址

4.将JSON对象转换成DataFrame或者其他数据结构

将字典构成的列表传入到DataFrame中来构造函数，并选出数据字段的子集。

pd.read_json可以自动将JSON数据集按照指定次序转换为Series或者DataFrame

从pandas中将数据导出为JSON格式,to_json方法

5.总结

如果你要处理的是文件而不是字符串，使用 json.dump() 和 json.load() 来编码和解码JSON数据；反之亦然

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