Python 解析库json及jsonpath pickle的实现方法是什么
小编给大家分享一下Python 解析库json及jsonpath pickle的实现方法是什么,相信大部分人都还不怎么了解,因此分享这篇文章给大家参考一下,希望大家阅读完这篇文章后大有收获,下面让我们一起去了解一下吧!
1. 数据抽取的概念2. 数据的分类3. JSON数据概述及解析3.1 JSON数据格式3.2 解析库jsonjson模块
是Python内置标准库,主要可以完成两个功能:序列化和反序列化。JSON对象和Python对象映射图如下:
3.2.1 json序列化
对象(字典/列表) 通过 json.dump()/json.dumps()
==> json字符串。示例代码如下:
import jsonclass Phone(object): def __init__(self, name, price): self.name = name self.price = priceclass Default(json.JSONEncoder): def default(self, o): print(o) # o: <__main__.Phone object at 0x10aa52c90> return [o.name, o.price]def parse(obj): print(obj) return {"name": obj.name, "price": obj.price}person_info_dict = { "name": "Amo", "age": 18, "is_boy": True, # "n": float("nan"), # float("nan"):NaN float("inf")=>Infinity float("-inf")=>-Infinity "phone": Phone("苹果8plus", 6458), "hobby": ("sing", "dance"), "dog": { "name": "藏獒", "age": 5, "color": "棕色", "isVIP": True, "child": None },}"""obj:需要序列化的对象 字典/列表 这里指的是person_info_dictindent: 缩进 单位: 字符sort_keys: 是否按key排序 默认是False不排序cls: json.JSONEncoder子类 处理不能序列化的对象ensure_ascii: 是否确保ascii编码 默认是True确保 "苹果8plus"==>"\u82f9\u679c8plus" 所以改为Falsedefault: 对象不能被序列化时,调用对应的函数解析"""# 将结果返回给一个变量result = json.dumps(person_info_dict, indent=2, sort_keys=True, ensure_ascii=False, # cls=Default, default=parse, # allow_nan=False 是否处理特殊常量值 # 默认为True 但是JSON标准规范不支持NaN, Infinity和-Infinity )print(result)with open("dump.json", "w", encoding="utf8") as file: # json.dump是将序列化后的内容存储到文件中 其他参数用法和dumps一致 json.dump(person_info_dict, file, indent=4, ensure_ascii=False, default=parse)
3.2.2 json反序列化
json字符串通过json.load()/json.loads()
==> 对象(字典/列表),示例代码如下:
import jsonclass Phone(object): def __init__(self, name, price): self.name = name self.price = pricedef pi(num): return int(num) + 1def oh(dic): if "price" in dic.keys(): return Phone(dic["name"], dic["price"]) return dicdef oph(*args, **kwargs): print(*args, **kwargs)# 我自己本地有一个dump.json文件with open("dump.json", "r", encoding="utf8") as file: # content = file.read() # parse_int/float: 整数/浮点数钩子函数 # object_hook: 对象解析钩子函数 将字典转为特定对象 传递给函数的是字典对象 # object_pairs_hook: 转化为特定对象 传递的是元组列表 # parse_constant: 常量钩子函数 NaN/Infinity/-Infinity # result = json.loads(content, object_hook=oh, parse_int=pi, object_pairs_hook=oph) result = json.load(file, parse_int=pi, object_hook=oh) # 直接将文件对象传入 print(type(result)) # <class 'dict'> print(result)4. jsonpath
jsonpath
三方库,点击这里这里进入官网,通过路径表达式,来快速获取字典当中的指定数据,灵感来自xpath表达式。命令安装:
pip install --user -i http://pypi.douban.com/simple --trusted-host pypi.douban.com jsonpath
或者:
语法格式如下:
from jsonpath import jsonpathdic = {....} # 要找数据的字典jsonpath(dic, 表达式)
常用的表达式语法如下:
案例一用到的字典如下:
dic = { "person": { "name": "Amo", "age": 18, "dog": [{ "name": "小花", "color": "red", "age": 6, "isVIP": True }, { "name": "小黑", "color": "black", "age": 2 }] }}
将上述抽象成一个树形结构如图所示:
需求及结果如下:
代码如下:
from jsonpath import jsonpathdic = { "person": { "name": "Amo", "age": 18, "dog": [{ "name": "小花", "color": "red", "age": 6, "isVIP": True }, { "name": "小黑", "color": "black", "age": 2 }] }}# 1.获取人的年龄print(jsonpath(dic, "$.person.age")) # 获取到数据返回一个列表 否则返回False# 2.获取第2个小狗的年龄print(jsonpath(dic, "$..dog[1].age"))# 3.获取所有小狗的年龄print(jsonpath(dic, "$..dog[0,1].age"))print(jsonpath(dic, "$..dog[*].age"))# 4.获取是VIP的小狗print(jsonpath(dic, "$..dog[?(@.isVIP)]"))# 5.获取年龄大于2的小狗print(jsonpath(dic, "$..dog[?(@.age>2)]"))# 6.获取最后一个小狗print(jsonpath(dic, "$..dog[-1:]"))print(jsonpath(dic, "$..dog[(@.length-1)]"))
上述代码执行结果如下:
案例二用到的字典如下:
book_dict = { "store": { "book": [ {"category": "reference", "author": "Nigel Rees", "title": "Sayings of the Century", "price": 8.95 }, {"category": "fiction", "author": "Evelyn Waugh", "title": "Sword of Honour", "price": 12.99 }, {"category": "fiction", "author": "Herman Melville", "title": "Moby Dick", "isbn": "0-553-21311-3", "price": 8.99 }, {"category": "fiction", "author": "J. R. R. Tolkien", "title": "The Lord of the Rings", "isbn": "0-395-19395-8", "price": 22.99 } ], "bicycle": { "color": "red", "price": 19.95 } }}
将上述抽象成一个树形结构如图所示:
需求及结果如下:
代码如下:
from jsonpath import jsonpathbook_dict = { "store": { "book": [ {"category": "reference", "author": "Nigel Rees", "title": "Sayings of the Century", "price": 8.95 }, {"category": "fiction", "author": "Evelyn Waugh", "title": "Sword of Honour", "price": 12.99 }, {"category": "fiction", "author": "Herman Melville", "title": "Moby Dick", "isbn": "0-553-21311-3", "price": 8.99 }, {"category": "fiction", "author": "J. R. R. Tolkien", "title": "The Lord of the Rings", "isbn": "0-395-19395-8", "price": 22.99 } ], "bicycle": { "color": "red", "price": 19.95 } }}# 1.store中的所有的book的作者print(jsonpath(book_dict, "$.store.book[*].author"))print(jsonpath(book_dict, "$..author"))# 2.store下的所有的元素print(jsonpath(book_dict, "$.store[*]"))print(jsonpath(book_dict, "$.store.*"))# 3.store中的所有的内容的价格print(jsonpath(book_dict, "$..price"))# 4.第三本书print(jsonpath(book_dict, "$..book[2]"))# 5.最后一本书print(jsonpath(book_dict, "$..book[-1:]"))print(jsonpath(book_dict, "$..book[(@.length-1)]"))# 6.前两本书print(jsonpath(book_dict, "$..book[0:2]"))# 7.获取有isbn的所有书print(jsonpath(book_dict, "$.store.book[?(@.isbn)]"))# 8.获取价格大于10的所有的书print(jsonpath(book_dict, "$.store.book[?(@.price>10)]"))# 9.获取所有的数据print(jsonpath(book_dict, "$..*"))5. Python专用JSON解析库pickle
pickle
处理的json对象不通用,可以额外的把函数给序列化。示例代码如下:
import pickledef eat(): print("Amo在努力地写博客~")person_info_dict = { "name": "Amo", "age": 18, "eat": eat}# print(pickle.dumps(person_info_dict))with open("pickle_json", "wb") as file: pickle.dump(person_info_dict, file)with open("pickle_json", "rb") as file: result = pickle.load(file) result["eat"]()JsonPath与XPath语法对比:
Json结构清晰,可读性高,复杂度低,非常容易匹配,下表中对应了XPath的用法。
以上是Python 解析库json及jsonpath pickle的实现方法是什么的所有内容,感谢各位的阅读!相信大家都有了一定的了解,希望分享的内容对大家有所帮助,如果还想学习更多知识,欢迎关注亿速云行业资讯频道!
声明:本站所有文章资源内容,如无特殊说明或标注,均为采集网络资源。如若本站内容侵犯了原著者的合法权益,可联系本站删除。